diff --git a/1-Introduction/1-intro-to-ML/README.md b/1-Introduction/1-intro-to-ML/README.md
index 715843ff34104e3bac2065574148252863884126..36ee822cc7b7ffc0660b5803da815d8f04c23b02 100644
--- a/1-Introduction/1-intro-to-ML/README.md
+++ b/1-Introduction/1-intro-to-ML/README.md
@@ -8,7 +8,7 @@ Watch the video, then take the pre-lesson quiz
> 🎥 Click the image above for a video discussing the difference between machine learning, AI, and deep learning.
-## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1/)
+## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/1/)
---
@@ -134,7 +134,7 @@ In the near future, understanding the basics of machine learning is going to be
Sketch, on paper or using an online app like [Excalidraw](https://excalidraw.com/), your understanding of the differences between AI, ML, deep learning, and data science. Add some ideas of problems that each of these techniques are good at solving.
-# [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2/)
+# [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/2/)
---
# Review & Self Study
diff --git a/1-Introduction/1-intro-to-ML/translations/README.bn.md b/1-Introduction/1-intro-to-ML/translations/README.bn.md
index 97e6b1e8f3c509684e8759daaa00b1828cc9d10d..3eaed0fed16a907b32d467fe5fe8600c108b0aa0 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.bn.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.bn.md
@@ -7,7 +7,7 @@ Watch the video, then take the pre-lesson quiz
> 🎥 মেশিন লার্নিং, এআই(আর্টিফিশিয়াল ইন্টিলিজেন্স) এবং ডিপ লার্নিং এর মধ্যে পার্থক্য এর আলোচনা জানতে উপরের ছবিটিতে ক্লিক করে ভিডিওটি দেখুন।
-## [প্রি-লেকচার-কুইজ](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1/)
+## [প্রি-লেকচার-কুইজ](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/1/)
---
বিগিনারদের জন্য ক্লাসিক্যাল মেশিন লার্নিং কোর্স এ আপনাকে স্বাগতম!আপনি হয় এই বিষয়ে সম্পূর্ণ নতুন অথবা মেশিন লার্নিং এ নিজের অনুশীলনকে আরও উন্নত করতে চান, আপনি আমাদের সাথে যোগদান করতে পেরে আমরা খুশি! আমরা আপনার ML অধ্যয়নের জন্য একটি বন্ধুত্বপূর্ণ লঞ্চিং স্পট তৈরি করতে চাই এবং আপনার মূল্যায়ন, প্রতিক্রিয়া,[ফিডব্যাক](https://github.com/microsoft/ML-For-Beginners/discussions). জানাতে এবং অন্তর্ভুক্ত করতে পেরে খুশি হব ।
@@ -136,7 +136,7 @@ MIT এর জন গাটেং মেশিন লার্নিং এর
স্কেচ, কাগজে বা একটি অনলাইন অ্যাপ ব্যবহার করে [এক্সালিড্র](https://excalidraw.com/) AI, ML, ডিপ লার্নিং এবং ডেটা সায়েন্সের মধ্যে পার্থক্য সম্পর্কে।
-# [লেকচার-কুইজ](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2/)
+# [লেকচার-কুইজ](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/2/)
---
# পর্যালোচনা ও সেল্ফ স্টাডি
diff --git a/1-Introduction/1-intro-to-ML/translations/README.es.md b/1-Introduction/1-intro-to-ML/translations/README.es.md
index 28cfa2e9166f1c01cd774cf5e37e2abbaed46237..fd193a0dc0781ef2e7add4dce4b4f1242baf058b 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.es.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.es.md
@@ -4,7 +4,7 @@
> 🎥 Haz clic en la imagen de arriba para ver un video donde se discuten las diferencias entre el machine learning, la inteligencia artificial, y el deep learning.
-## [Cuestionario previo a la conferencia](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1?loc=es)
+## [Cuestionario previo a la conferencia](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/1?loc=es)
### Introducción
@@ -100,7 +100,7 @@ En el futuro próximo, entender las bases de machine learning va a ser una neces
Dibuja, en papel o usando una aplicación como [Excalidraw](https://excalidraw.com/), cómo entiendes las diferencias entre inteligencia artificial, ML, deep learning, y la ciencia de datos. Agrega algunas ideas de problemas que cada una de estas técnicas son buenas en resolver.
-## [Cuestionario después de la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2?loc=es)
+## [Cuestionario después de la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/2?loc=es)
## Revisión y autoestudio
diff --git a/1-Introduction/1-intro-to-ML/translations/README.fr.md b/1-Introduction/1-intro-to-ML/translations/README.fr.md
index 90798160697f94de2e57bfdf0aad7c11a351fea0..717fedf96cb1c5d63f8db5a8d40fe0f86d37a5f9 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.fr.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.fr.md
@@ -4,7 +4,7 @@
> 🎥 Cliquer sur l'image ci-dessus afin de regarder une vidéo expliquant la différence entre machine learning, AI et deep learning.
-## [Quiz préalable](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1?loc=fr)
+## [Quiz préalable](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/1?loc=fr)
### Introduction
@@ -98,7 +98,7 @@ Dans un avenir proche, comprendre les bases du machine learning sera indispensab
Esquisser, sur papier ou à l'aide d'une application en ligne comme [Excalidraw](https://excalidraw.com/), votre compréhension des différences entre l'IA, le ML, le deep learning et la data science. Ajouter quelques idées de problèmes que chacune de ces techniques est bonne à résoudre.
-## [Quiz de validation des connaissances](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2?loc=fr)
+## [Quiz de validation des connaissances](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/2?loc=fr)
## Révision et auto-apprentissage
diff --git a/1-Introduction/1-intro-to-ML/translations/README.id.md b/1-Introduction/1-intro-to-ML/translations/README.id.md
index 69a9157b598bcc97d96a66caf3f805b872d80eed..623e9fd1836d197413bdb56b5f0b861c25a47c68 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.id.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.id.md
@@ -4,7 +4,7 @@
> 🎥 Klik gambar diatas untuk menonton video yang mendiskusikan perbedaan antara Machine Learning, AI, dan Deep Learning.
-## [Quiz Pra-Pelajaran](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1/)
+## [Quiz Pra-Pelajaran](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/1/)
### Pengantar
@@ -96,7 +96,7 @@ Dalam waktu dekat, memahami dasar-dasar Machine Learning akan menjadi suatu keha
Buat sketsa di atas kertas atau menggunakan aplikasi seperti [Excalidraw](https://excalidraw.com/), mengenai pemahaman kamu tentang perbedaan antara AI, ML, Deep Learning, dan Data Science. Tambahkan beberapa ide masalah yang cocok diselesaikan masing-masing teknik.
-## [Quiz Pasca-Pelajaran](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2/)
+## [Quiz Pasca-Pelajaran](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/2/)
## Ulasan & Belajar Mandiri
diff --git a/1-Introduction/1-intro-to-ML/translations/README.it.md b/1-Introduction/1-intro-to-ML/translations/README.it.md
index eab0f4907829ce243c95c9f42b994dd66edd9cd8..b7806dcbed08abe0407d32f94c8e5bd44dac4ef0 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.it.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.it.md
@@ -4,7 +4,7 @@
> 🎥 Fare clic sull'immagine sopra per un video che illustra la differenza tra machine learning, intelligenza artificiale (AI) e deep learning.
-## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1/?loc=it)
+## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/1/?loc=it)
### Introduzione
@@ -97,7 +97,7 @@ Nel prossimo futuro, comprendere le basi di machine learning sarà un must per l
Disegnare, su carta o utilizzando un'app online come [Excalidraw](https://excalidraw.com/), la propria comprensione delle differenze tra AI, ML, deep learning e data science. Aggiungere alcune idee sui problemi che ciascuna di queste tecniche è in grado di risolvere.
-## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2/?loc=it)
+## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/2/?loc=it)
## Revisione e Auto Apprendimento
diff --git a/1-Introduction/1-intro-to-ML/translations/README.ja.md b/1-Introduction/1-intro-to-ML/translations/README.ja.md
index b88738d03e2b7908fd8275f435fefd7ac140d621..563abdea7bd7879976e56c810bcbfd048a9351ab 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.ja.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.ja.md
@@ -4,7 +4,7 @@
> 🎥 上の画像をクリックすると、機械学習、AI、深層学習の違いについて説明した動画が表示されます。
-## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1?loc=ja)
+## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/1?loc=ja)
### イントロダクション
@@ -94,7 +94,7 @@
## 🚀 Challenge
AI、ML、深層学習、データサイエンスの違いについて理解していることを、紙や[Excalidraw](https://excalidraw.com/)などのオンラインアプリを使ってスケッチしてください。また、それぞれの技術が得意とする問題のアイデアを加えてみてください。
-## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2?loc=ja)
+## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/2?loc=ja)
## 振り返りと自習
diff --git a/1-Introduction/1-intro-to-ML/translations/README.ko.md b/1-Introduction/1-intro-to-ML/translations/README.ko.md
index e19a23a8434d45969895b116bcf07f27ec087b2b..b62f15771ae94e40cad92aaefae56b371ae95fa2 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.ko.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.ko.md
@@ -4,7 +4,7 @@
> 🎥 머신러닝, AI 그리고 딥러닝의 차이를 설명하는 영상을 보려면 위 이미지를 클릭합니다.
-## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1/)
+## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/1/)
### 소개
@@ -100,7 +100,7 @@
종이에 그리거나, [Excalidraw](https://excalidraw.com/)처럼 온라인 앱을 이용하여 AI, ML, 딥러닝, 그리고 데이터 사이언스의 차이를 이해합시다. 각 기술들이 잘 해결할 수 있는 문제에 대해 아이디어를 합쳐보세요.
-## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2/)
+## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/2/)
## 리뷰 & 자기주도 학습
diff --git a/1-Introduction/1-intro-to-ML/translations/README.pt-br.md b/1-Introduction/1-intro-to-ML/translations/README.pt-br.md
index 644a2bb0cb17800b11528802df8cc41ffb55fadb..1f0d51ef8b5d485cbbfcc7d9a932aea30e731a54 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.pt-br.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.pt-br.md
@@ -4,7 +4,7 @@
> 🎥 Clique na imagem acima para assistir um vídeo que ilustra a diferença entre machine learning, AI, e deep learning.
-## [Questionário inicial](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1?loc=ptbr)
+## [Questionário inicial](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/1?loc=ptbr)
### Introdução
@@ -100,7 +100,7 @@ Em um futuro próximo, compreender os fundamentos do machine learning será uma
Esboce, no papel ou usando um aplicativo online como [Excalidraw](https://excalidraw.com/), sua compreensão das diferenças entre AI, ML, deep learning e data science. Adicione algumas idéias de problemas que cada uma dessas técnicas é boa para resolver.
-## [Questionário pós-aula](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2?loc=ptbr)
+## [Questionário pós-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/2?loc=ptbr)
## Revisão e autoestudo
diff --git a/1-Introduction/1-intro-to-ML/translations/README.ru.md b/1-Introduction/1-intro-to-ML/translations/README.ru.md
index eeebc7dfffd070445268584f49377756ac54c081..c61b5a52349039fae02bdd7ba884823d7294ee86 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.ru.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.ru.md
@@ -8,7 +8,7 @@
> 🎥 Нажмите на изображение выше, чтобы просмотреть видео, в котором обсуждается разница между машинным обучением, искусственным интеллектом и глубоким обучением.
-## [Тест перед лекцией](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1/)
+## [Тест перед лекцией](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/1/)
---
@@ -134,7 +134,7 @@
Набросайте на бумаге или с помощью онлайн-приложения, такого как [Excalidraw](https://excalidraw.com/), ваше понимание различий между AI, ML, глубоким обучением и наукой о данных. Добавьте несколько идей о проблемах, которые может решить каждый из этих методов.
-# [Тест после лекции](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2/)
+# [Тест после лекции](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/2/)
---
# Обзор и самообучение
diff --git a/1-Introduction/1-intro-to-ML/translations/README.tr.md b/1-Introduction/1-intro-to-ML/translations/README.tr.md
index 669e649de9301700020b8dcec54910854d01b161..82dd91e2cea5ecf10f6099dbe92502dc62854b14 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.tr.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.tr.md
@@ -4,7 +4,7 @@
> 🎥 Makine öğrenimi, yapay zeka ve derin öğrenme arasındaki farkı tartışan bir video için yukarıdaki resme tıklayın.
-## [Ders öncesi sınav](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1?loc=tr)
+## [Ders öncesi sınav](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/1?loc=tr)
### Introduction
@@ -103,7 +103,7 @@ Yakın gelecekte, yaygın olarak benimsenmesi nedeniyle makine öğreniminin tem
Kağıt üzerinde veya [Excalidraw](https://excalidraw.com/) gibi çevrimiçi bir uygulama kullanarak AI, makine öğrenimi, derin öğrenme ve veri bilimi arasındaki farkları anladığınızdan emin olun. Bu tekniklerin her birinin çözmede iyi olduğu bazı problem fikirleri ekleyin.
-## [Ders sonrası test](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2?loc=tr)
+## [Ders sonrası test](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/2?loc=tr)
## İnceleme ve Bireysel Çalışma
diff --git a/1-Introduction/1-intro-to-ML/translations/README.zh-cn.md b/1-Introduction/1-intro-to-ML/translations/README.zh-cn.md
index a2959603647ad4965fd11237983713a76b254c52..22133bd4e662b1ff0113b9b8c5d3c213ba5b98e9 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.zh-cn.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.zh-cn.md
@@ -4,7 +4,7 @@
> 🎥 点击上面的图片观看讨论机器学习、人工智能和深度学习之间区别的视频。
-## [课前测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1/)
+## [课前测验](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/1/)
### 介绍
@@ -96,7 +96,7 @@
在纸上或使用 [Excalidraw](https://excalidraw.com/) 等在线应用程序绘制草图，了解你对 AI、ML、深度学习和数据科学之间差异的理解。添加一些关于这些技术擅长解决的问题的想法。
-## [阅读后测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2/)
+## [阅读后测验](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/2/)
## 复习与自学
diff --git a/1-Introduction/1-intro-to-ML/translations/README.zh-tw.md b/1-Introduction/1-intro-to-ML/translations/README.zh-tw.md
index 0d08153f0664dfd25e2e8b81f1ac2eddacc05f58..9e55679b6c7188e0bf41d7fb2aec4b38d8974c3f 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.zh-tw.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.zh-tw.md
@@ -3,7 +3,7 @@
[](https://youtu.be/lTd9RSxS9ZE "機器學習，人工智能，深度學習-有什麽區別?")
> 🎥 點擊上面的圖片觀看討論機器學習、人工智能和深度學習之間區別的視頻。
-## [課前測驗](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1/)
+## [課前測驗](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/1/)
### 介紹
@@ -92,7 +92,7 @@
在紙上或使用 [Excalidraw](https://excalidraw.com/) 等在線應用程序繪製草圖，了解你對 AI、ML、深度學習和數據科學之間差異的理解。添加一些關於這些技術擅長解決的問題的想法。
-## [閱讀後測驗](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2/)
+## [閱讀後測驗](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/2/)
## 復習與自學
diff --git a/1-Introduction/2-history-of-ML/README.md b/1-Introduction/2-history-of-ML/README.md
index 53ca9ee77921dbf64f8d2efcee027cafe97acac5..a26fce77174f1c7679acf4b5917c675f982b881e 100644
--- a/1-Introduction/2-history-of-ML/README.md
+++ b/1-Introduction/2-history-of-ML/README.md
@@ -3,7 +3,7 @@
> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3/)
+## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/3/)
---
@@ -128,7 +128,7 @@ It remains to be seen what the future holds, but it is important to understand t
Dig into one of these historical moments and learn more about the people behind them. There are fascinating characters, and no scientific discovery was ever created in a cultural vacuum. What do you discover?
-## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4/)
+## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/4/)
---
## Review & Self Study
diff --git a/1-Introduction/2-history-of-ML/translations/README.es.md b/1-Introduction/2-history-of-ML/translations/README.es.md
index 7ef7d80ae891eaf85079cf52eb1a642ae7a90352..b878e4e10558a60136224a18389bdb6493d712c8 100755
--- a/1-Introduction/2-history-of-ML/translations/README.es.md
+++ b/1-Introduction/2-history-of-ML/translations/README.es.md
@@ -3,7 +3,7 @@
> Boceto por [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [Cuestionario previo a la conferencia](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3?loc=es)
+## [Cuestionario previo a la conferencia](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/3?loc=es)
En esta lección, analizaremos los principales hitos en la historia del machine learning y la inteligencia artificial.
@@ -102,7 +102,7 @@ Queda por ver qué depara el futuro, pero es importante entender estos sistemas
Sumérjase dentro de unos de estos momentos históricos y aprenda más sobre las personas detrás de ellos. Hay personajes fascinantes y nunca ocurrió ningún descubrimiento científico en un vacío cultural. ¿Qué descubres?
-## [Cuestionario posterior a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4?loc=es)
+## [Cuestionario posterior a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/4?loc=es)
## Revisión y autoestudio
diff --git a/1-Introduction/2-history-of-ML/translations/README.fr.md b/1-Introduction/2-history-of-ML/translations/README.fr.md
index efe268777f678ad8042774c0f12b3007c2726d8b..9c6c66873ed04ef9178098e9dee0abc62f7f609d 100644
--- a/1-Introduction/2-history-of-ML/translations/README.fr.md
+++ b/1-Introduction/2-history-of-ML/translations/README.fr.md
@@ -3,7 +3,7 @@
> Sketchnote de [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [Quizz préalable](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3?loc=fr)
+## [Quizz préalable](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/3?loc=fr)
Dans cette leçon, nous allons parcourir les principales étapes de l'histoire du machine learning et de l'intelligence artificielle.
@@ -102,7 +102,7 @@ Reste à savoir ce que l'avenir nous réserve, mais il est important de comprend
Plongez dans l'un de ces moments historiques et apprenez-en plus sur les personnes derrière ceux-ci. Il y a des personnalités fascinantes, et aucune découverte scientifique n'a jamais été créée avec un vide culturel. Que découvrez-vous ?
-## [Quiz de validation des connaissances](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4?loc=fr)
+## [Quiz de validation des connaissances](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/4?loc=fr)
## Révision et auto-apprentissage
diff --git a/1-Introduction/2-history-of-ML/translations/README.id.md b/1-Introduction/2-history-of-ML/translations/README.id.md
index 9e695a8a9a976b44197b557d19b22423e783ba89..47ce2816ca3606c301a265976e129767ce00a5f1 100644
--- a/1-Introduction/2-history-of-ML/translations/README.id.md
+++ b/1-Introduction/2-history-of-ML/translations/README.id.md
@@ -3,7 +3,7 @@
> Catatan sketsa oleh [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [Quiz Pra-Pelajaran](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3/)
+## [Quiz Pra-Pelajaran](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/3/)
Dalam pelajaran ini, kita akan membahas tonggak utama dalam sejarah Machine Learning dan Artificial Intelligence.
@@ -101,7 +101,7 @@ Kita masih belum tahu apa yang akan terjadi di masa depan, tetapi penting untuk
Gali salah satu momen bersejarah ini dan pelajari lebih lanjut tentang orang-orang di baliknya. Ada karakter yang menarik, dan tidak ada penemuan ilmiah yang pernah dibuat dalam kekosongan budaya. Apa yang kamu temukan?
-## [Quiz Pasca-Pelajaran](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4/)
+## [Quiz Pasca-Pelajaran](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/4/)
## Ulasan & Belajar Mandiri
diff --git a/1-Introduction/2-history-of-ML/translations/README.it.md b/1-Introduction/2-history-of-ML/translations/README.it.md
index e6fc2d900f79891fbe3616992d95414f3eed945b..f4f678aa19500467e3e62a988703452614662784 100644
--- a/1-Introduction/2-history-of-ML/translations/README.it.md
+++ b/1-Introduction/2-history-of-ML/translations/README.it.md
@@ -3,7 +3,7 @@
> Sketchnote di [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3/?loc=it)
+## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/3/?loc=it)
In questa lezione, si camminerà attraverso le principali pietre miliari nella storia di machine learning e dell'intelligenza artificiale.
@@ -103,7 +103,7 @@ Resta da vedere cosa riserva il futuro, ma è importante capire questi sistemi i
Approfondire uno di questi momenti storici e scoprire
di più sulle persone che stanno dietro ad essi. Ci sono personaggi affascinanti e nessuna scoperta scientifica è mai stata creata in un vuoto culturale. Cosa si è scoperto?
-## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4/?loc=it)
+## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/4/?loc=it)
## Revisione e Auto Apprendimento
diff --git a/1-Introduction/2-history-of-ML/translations/README.ja.md b/1-Introduction/2-history-of-ML/translations/README.ja.md
index 6ba32096aa5b124a0a3d565936a6625f131f3c2d..c3eeedb20d9ce7711889759922f4447652d0ef89 100644
--- a/1-Introduction/2-history-of-ML/translations/README.ja.md
+++ b/1-Introduction/2-history-of-ML/translations/README.ja.md
@@ -3,7 +3,7 @@
> [Tomomi Imura](https://www.twitter.com/girlie_mac)によるスケッチ
-## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3?loc=ja)
+## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/3?loc=ja)
この授業では、機械学習と人工知能の歴史における主要な出来事を紹介します。
@@ -99,7 +99,7 @@
これらの歴史的瞬間の1つを掘り下げて、その背後にいる人々について学びましょう。魅力的な人々がいますし、文化的に空白の状態で科学的発見がなされたことはありません。どういったことが見つかるでしょうか？
-## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4?loc=ja)
+## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/4?loc=ja)
## 振り返りと自習
diff --git a/1-Introduction/2-history-of-ML/translations/README.ko.md b/1-Introduction/2-history-of-ML/translations/README.ko.md
index d630201e2115695caffff9c80464ceaf6f174244..3c61b5e771cd6220fbfc798b64d42814a6f01d89 100644
--- a/1-Introduction/2-history-of-ML/translations/README.ko.md
+++ b/1-Introduction/2-history-of-ML/translations/README.ko.md
@@ -3,7 +3,7 @@
> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3/)
+## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/3/)
이 강의에서, 머신러닝과 인공 지능의 역사에서 주요 마일스톤을 살펴보려 합니다.
@@ -103,7 +103,7 @@ natural language processing 연구가 발전하고, 검색이 개선되어 더
역사적인 순간에 사람들 뒤에서 한 가지를 집중적으로 파고 있는 자를 자세히 알아보세요. 매력있는 캐릭터가 있으며, 문화가 사라진 곳에서는 과학적인 발견을 하지 못합니다. 당신은 어떤 발견을 해보았나요?
-## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4/)
+## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/4/)
## 검토 & 자기주도 학습
diff --git a/1-Introduction/2-history-of-ML/translations/README.pt-br.md b/1-Introduction/2-history-of-ML/translations/README.pt-br.md
index 57ae435dd8897d8d7963499050edb85001b1d730..d9fa1471f01374e9c9bd0f52a92673de29b7f998 100644
--- a/1-Introduction/2-history-of-ML/translations/README.pt-br.md
+++ b/1-Introduction/2-history-of-ML/translations/README.pt-br.md
@@ -3,7 +3,7 @@
> Sketchnote por [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [Teste pré-aula](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3?loc=ptbr)
+## [Teste pré-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/3?loc=ptbr)
Nesta lição, veremos os principais marcos da história do machine learning e da artificial intelligence.
@@ -103,7 +103,7 @@ Resta saber o que o futuro reserva, mas é importante entender esses sistemas de
Explore um desses momentos históricos e aprenda mais sobre as pessoas por trás deles. Existem personagens fascinantes e nenhuma descoberta científica foi criada em um vácuo cultural. O que você descobriu?
-## [Questionário pós-aula](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4?loc=ptbr)
+## [Questionário pós-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/4?loc=ptbr)
## Revisão e Autoestudo
diff --git a/1-Introduction/2-history-of-ML/translations/README.ru.md b/1-Introduction/2-history-of-ML/translations/README.ru.md
index 65c72724a4c5e71ad7276358dbe59c345f77dd51..5dbfa2cb8c4f9ed1388d241d5e7761e89383f880 100644
--- a/1-Introduction/2-history-of-ML/translations/README.ru.md
+++ b/1-Introduction/2-history-of-ML/translations/README.ru.md
@@ -3,7 +3,7 @@
> Заметка [Томоми Имура](https://www.twitter.com/girlie_mac)
-## [Тест перед лекцией](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3/)
+## [Тест перед лекцией](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/3/)
---
@@ -128,7 +128,7 @@
Погрузитесь в один из этих исторических моментов и узнайте больше о людях, стоящих за ними. Есть увлекательные персонажи, и ни одно научное открытие никогда не создавалось в культурном вакууме. Что вы обнаружите?
-## [Тест после лекции](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4/)
+## [Тест после лекции](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/4/)
---
## Обзор и самообучение
diff --git a/1-Introduction/2-history-of-ML/translations/README.tr.md b/1-Introduction/2-history-of-ML/translations/README.tr.md
index af2346fb7e95579ecc1d2fe3a11e302bda44c05a..d9277d40b41c88c765ccba7e4eeadde61c1d0d4f 100644
--- a/1-Introduction/2-history-of-ML/translations/README.tr.md
+++ b/1-Introduction/2-history-of-ML/translations/README.tr.md
@@ -3,7 +3,7 @@
> [Tomomi Imura](https://www.twitter.com/girlie_mac) tarafından hazırlanan taslak-not
-## [Ders öncesi test](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3?loc=tr)
+## [Ders öncesi test](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/3?loc=tr)
Bu derste, makine öğrenimi ve yapay zeka tarihindeki önemli kilometre taşlarını inceleyeceğiz.
@@ -102,7 +102,7 @@ Geleceğin neler getireceğini birlikte göreceğiz, ancak bu bilgisayar sisteml
Bu tarihi anlardan birine girin ve arkasındaki insanlar hakkında daha fazla bilgi edinin. Büyüleyici karakterler var ve kültürel bir boşlukta hiçbir bilimsel keşif yaratılmadı. Ne keşfedersiniz?
-## [Ders sonrası test](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4?loc=tr)
+## [Ders sonrası test](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/4?loc=tr)
## İnceleme ve Bireysel Çalışma
diff --git a/1-Introduction/2-history-of-ML/translations/README.zh-cn.md b/1-Introduction/2-history-of-ML/translations/README.zh-cn.md
index 700c13200961b0f86c76eabe60b89a36430b6a52..e1184b6d44af7aa4a7af581dda18c94553f26cf1 100644
--- a/1-Introduction/2-history-of-ML/translations/README.zh-cn.md
+++ b/1-Introduction/2-history-of-ML/translations/README.zh-cn.md
@@ -3,7 +3,7 @@
> 作者 [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [课前测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3/)
+## [课前测验](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/3/)
在本课中，我们将走过机器学习和人工智能历史上的主要里程碑。
@@ -101,7 +101,7 @@ Alan Turing，一个真正杰出的人，[在 2019 年被公众投票选出](htt
深入了解这些历史时刻之一，并更多地了解它们背后的人。这里有许多引人入胜的人物，没有一项科学发现是在文化真空中创造出来的。你发现了什么？
-## [课后测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4/)
+## [课后测验](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/4/)
## 复习与自学
diff --git a/1-Introduction/2-history-of-ML/translations/README.zh-tw.md b/1-Introduction/2-history-of-ML/translations/README.zh-tw.md
index 58915f85e319a440cf68178e6f9d6b2b0a7e4e03..4fb491d2f9eac02b1960f2ddc2c1951a3175e58a 100644
--- a/1-Introduction/2-history-of-ML/translations/README.zh-tw.md
+++ b/1-Introduction/2-history-of-ML/translations/README.zh-tw.md
@@ -2,7 +2,7 @@
> 作者 [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [課前測驗](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3/)
+## [課前測驗](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/3/)
在本課中，我們將走過機器學習和人工智能歷史上的主要裏程碑。
@@ -95,7 +95,7 @@ Alan Turing，一個真正傑出的人，[在 2019 年被公眾投票選出](htt
深入了解這些歷史時刻之一，並更多地了解它們背後的人。這裏有許多引人入勝的人物，沒有一項科學發現是在文化真空中創造出來的。你發現了什麽？
-## [課後測驗](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4/)
+## [課後測驗](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/4/)
## 復習與自學
diff --git a/1-Introduction/3-fairness/README.md b/1-Introduction/3-fairness/README.md
index d4ebfdf244baa96319fdaf292bcc57af6efbaeba..baa2756279e2b059041633399fa85c48784d8b5a 100644
--- a/1-Introduction/3-fairness/README.md
+++ b/1-Introduction/3-fairness/README.md
@@ -3,7 +3,7 @@
> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/5/)
+## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/5/)
## Introduction
@@ -184,7 +184,7 @@ To prevent biases from being introduced in the first place, we should:
Think about real-life scenarios where unfairness is evident in model-building and usage. What else should we consider?
-## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/6/)
+## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/6/)
## Review & Self Study
In this lesson, you have learned some basics of the concepts of fairness and unfairness in machine learning.
diff --git a/1-Introduction/3-fairness/translations/README.es.md b/1-Introduction/3-fairness/translations/README.es.md
index be09df6ba0bdcbc03ae06a788621d2b7a7b49972..d6c71df9cfd1448948c015acb71d93d5095598cc 100644
--- a/1-Introduction/3-fairness/translations/README.es.md
+++ b/1-Introduction/3-fairness/translations/README.es.md
@@ -3,7 +3,7 @@
> Sketchnote por [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [Examen previo a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/5?loc=es)
+## [Examen previo a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/5?loc=es)
## Introducción
@@ -183,7 +183,7 @@ Para prevenir que los sesgos sean introducidos en primer lugar, debemos:
Piensa en escenarios de la vida real donde la injusticia es evidente en la construcción y uso de modelos. ¿Qué más debemos considerar?
-## [Cuestionario posterior a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/6?loc=es)
+## [Cuestionario posterior a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/6?loc=es)
## Revisión y autoestudio
En esta lección has aprendido algunos de los conceptos básicos de justicia e injusticia en el aprendizaje automático.
diff --git a/1-Introduction/3-fairness/translations/README.fr.md b/1-Introduction/3-fairness/translations/README.fr.md
index c7735721add9e3685433fb06d56b73c2c7d8f08a..73e2ef525d2f3056eb54aeddfbbe372413f99894 100644
--- a/1-Introduction/3-fairness/translations/README.fr.md
+++ b/1-Introduction/3-fairness/translations/README.fr.md
@@ -3,7 +3,7 @@
> Sketchnote par [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [Quiz préalable](https://white-water-09ec41f0f.azurestaticapps.net/quiz/5/?loc=fr)
+## [Quiz préalable](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/5/?loc=fr)
## Introduction
@@ -184,7 +184,7 @@ Pour éviter que des biais ne soient introduits en premier lieu, nous devrions
Pensez à des scénarios de la vie réelle où l'injustice est évidente dans la construction et l'utilisation de modèles. Que devrions-nous considérer d'autre ?
-## [Quiz de validation des connaissances](https://white-water-09ec41f0f.azurestaticapps.net/quiz/6/?loc=fr)
+## [Quiz de validation des connaissances](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/6/?loc=fr)
## Révision et auto-apprentissage
Dans cette leçon, nous avons appris quelques notions de base sur les concepts d'équité et d'injustice dans le machine learning.
diff --git a/1-Introduction/3-fairness/translations/README.id.md b/1-Introduction/3-fairness/translations/README.id.md
index 980cbd88d2716bacffee4678761cbe5014c9e27f..053960d8bc5e8f3795dfbfb4b2ec38e1cdc72792 100644
--- a/1-Introduction/3-fairness/translations/README.id.md
+++ b/1-Introduction/3-fairness/translations/README.id.md
@@ -3,7 +3,7 @@
> Catatan sketsa oleh [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [Quiz Pra-Pelajaran](https://white-water-09ec41f0f.azurestaticapps.net/quiz/5/)
+## [Quiz Pra-Pelajaran](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/5/)
## Pengantar
@@ -185,7 +185,7 @@ Untuk mencegah kemunculan bias pada awalnya, kita harus:
Pikirkan tentang skenario kehidupan nyata di mana ketidakadilan terbukti dalam pembuatan dan penggunaan model. Apa lagi yang harus kita pertimbangkan?
-## [Quiz Pasca-Pelajaran](https://white-water-09ec41f0f.azurestaticapps.net/quiz/6/)
+## [Quiz Pasca-Pelajaran](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/6/)
## Ulasan & Belajar Mandiri
Dalam pelajaran ini, Kamu telah mempelajari beberapa dasar konsep keadilan dan ketidakadilan dalam pembelajaran mesin.
diff --git a/1-Introduction/3-fairness/translations/README.it.md b/1-Introduction/3-fairness/translations/README.it.md
index 29ab88c31ddfcbe59930822022f1a3a19abbd061..a9440c90ee9ab336c33962d2a6cc83f7cdc40744 100644
--- a/1-Introduction/3-fairness/translations/README.it.md
+++ b/1-Introduction/3-fairness/translations/README.it.md
@@ -3,7 +3,7 @@
> Sketchnote di [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/5/?loc=it)
+## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/5/?loc=it)
## Introduzione
@@ -183,7 +183,7 @@ Per evitare che vengano introdotti pregiudizi, in primo luogo, si dovrebbe:
Si pensi a scenari di vita reale in cui l'ingiustizia è evidente nella creazione e nell'utilizzo del modello. Cos'altro si dovrebbe considerare?
-## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/6/?loc=it)
+## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/6/?loc=it)
## Revisione e Auto Apprendimento
diff --git a/1-Introduction/3-fairness/translations/README.ja.md b/1-Introduction/3-fairness/translations/README.ja.md
index ffa878c17192c4be70d26bbc058025947f95279c..e5a3d21ee407cc9d0c7945b3e34295c789f8c423 100644
--- a/1-Introduction/3-fairness/translations/README.ja.md
+++ b/1-Introduction/3-fairness/translations/README.ja.md
@@ -3,7 +3,7 @@
> [Tomomi Imura](https://www.twitter.com/girlie_mac)によるスケッチ
-## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/5?loc=ja)
+## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/5?loc=ja)
## イントロダクション
@@ -178,7 +178,7 @@ AIや機械学習における公平性の保証は、依然として複雑な社
モデルの構築や使用において、不公平が明らかになるような現実のシナリオを考えてみてください。他にどのようなことを考えるべきでしょうか？
-## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/6?loc=ja)
+## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/6?loc=ja)
## Review & Self Study
このレッスンでは、機械学習における公平、不公平の概念の基礎を学びました。
diff --git a/1-Introduction/3-fairness/translations/README.ko.md b/1-Introduction/3-fairness/translations/README.ko.md
index 7cbc8e3534e28c73da80f4c04a61f2946df6e099..4718dcc18f2f2a2981b330b1d8169ecca2d7e39b 100644
--- a/1-Introduction/3-fairness/translations/README.ko.md
+++ b/1-Introduction/3-fairness/translations/README.ko.md
@@ -3,7 +3,7 @@
> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/5/)
+## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/5/)
## 소개
@@ -185,7 +185,7 @@ AI와 머신러닝의 공정성을 보장하는 건 계속 복잡한 사회기
모델을 구축하고 사용하면서 불공정한 실-생활 시나리오를 생각해보세요. 어떻게 고려해야 하나요?
-## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/6/)
+## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/6/)
## 검토 & 자기주도 학습
diff --git a/1-Introduction/3-fairness/translations/README.pt-br.md b/1-Introduction/3-fairness/translations/README.pt-br.md
index 64a0cffc73fe93999dcc05ea677dd22e489cfe62..9bb5f629e19089a84d1fe0bafca0f2e57ff54ffa 100644
--- a/1-Introduction/3-fairness/translations/README.pt-br.md
+++ b/1-Introduction/3-fairness/translations/README.pt-br.md
@@ -3,7 +3,7 @@
> Sketchnote por [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [Teste pré-aula](https://white-water-09ec41f0f.azurestaticapps.net/quiz/5?loc=ptbr)
+## [Teste pré-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/5?loc=ptbr)
## Introdução
@@ -182,7 +182,7 @@ Para evitar que preconceitos sejam introduzidos em primeiro lugar, devemos:
Pense em cenários da vida real onde a injustiça é evidente na construção e uso de modelos. O que mais devemos considerar?
-## [Questionário pós-aula](https://white-water-09ec41f0f.azurestaticapps.net/quiz/6?loc=ptbr)
+## [Questionário pós-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/6?loc=ptbr)
## Revisão e Autoestudo
diff --git a/1-Introduction/3-fairness/translations/README.zh-cn.md b/1-Introduction/3-fairness/translations/README.zh-cn.md
index 5eec4587c7df57c1e1e1da9a5723fd59725cede4..b8f6fa3d142fc468ec279f19430b161791e122c3 100644
--- a/1-Introduction/3-fairness/translations/README.zh-cn.md
+++ b/1-Introduction/3-fairness/translations/README.zh-cn.md
@@ -3,7 +3,7 @@
> 作者 [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [课前测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/5/)
+## [课前测验](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/5/)
## 介绍
@@ -186,7 +186,7 @@
想想现实生活中的场景，在模型构建和使用中明显存在不公平。我们还应该考虑什么？
-## [课后测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/6/)
+## [课后测验](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/6/)
## 复习与自学
在本课中，你学习了机器学习中公平和不公平概念的一些基础知识。
diff --git a/1-Introduction/3-fairness/translations/README.zh-tw.md b/1-Introduction/3-fairness/translations/README.zh-tw.md
index 56df3122ab143154b9ca0ec70334d37407971251..db84f46d444e591326cc0b75a2747e696a216039 100644
--- a/1-Introduction/3-fairness/translations/README.zh-tw.md
+++ b/1-Introduction/3-fairness/translations/README.zh-tw.md
@@ -2,7 +2,7 @@
> 作者 [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [課前測驗](https://white-water-09ec41f0f.azurestaticapps.net/quiz/5/)
+## [課前測驗](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/5/)
## 介紹
@@ -181,7 +181,7 @@
想想現實生活中的場景，在模型構建和使用中明顯存在不公平。我們還應該考慮什麽？
-## [課後測驗](https://white-water-09ec41f0f.azurestaticapps.net/quiz/6/)
+## [課後測驗](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/6/)
## 復習與自學
在本課中，你學習了機器學習中公平和不公平概念的一些基礎知識。
diff --git a/1-Introduction/4-techniques-of-ML/README.md b/1-Introduction/4-techniques-of-ML/README.md
index 3b46b036ed79a65d8d20a32fe2ba13c18decf917..01e40faca5a1ff23e2642a35f8644f707cc5b9c5 100644
--- a/1-Introduction/4-techniques-of-ML/README.md
+++ b/1-Introduction/4-techniques-of-ML/README.md
@@ -5,7 +5,7 @@ The process of building, using, and maintaining machine learning models and the
- Understand the processes underpinning machine learning at a high level.
- Explore base concepts such as 'models', 'predictions', and 'training data'.
-## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/7/)
+## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/7/)
## Introduction
@@ -103,7 +103,7 @@ In these lessons, you will discover how to use these steps to prepare, build, te
Draw a flow chart reflecting the steps of a ML practitioner. Where do you see yourself right now in the process? Where do you predict you will find difficulty? What seems easy to you?
-## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/8/)
+## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/8/)
## Review & Self Study
diff --git a/1-Introduction/4-techniques-of-ML/translations/README.es.md b/1-Introduction/4-techniques-of-ML/translations/README.es.md
index eb7de48e6383d37d3a5fd884f1787ce8277762ad..e5458c133f07573ef6059b62d398dc80756db775 100755
--- a/1-Introduction/4-techniques-of-ML/translations/README.es.md
+++ b/1-Introduction/4-techniques-of-ML/translations/README.es.md
@@ -6,7 +6,7 @@ El proceso de creación, uso y mantenimiento de modelos de machine learning, y l
- Explorar conceptos básicos como 'modelos', 'predicciones', y 'datos de entrenamiento'
-## [Cuestionario previo a la conferencia](https://white-water-09ec41f0f.azurestaticapps.net/quiz/7?loc=es)
+## [Cuestionario previo a la conferencia](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/7?loc=es)
## Introducción
A un alto nivel, el arte de crear procesos de machine learning (ML) se compone de una serie de pasos:
@@ -101,7 +101,7 @@ En estas lecciones, descubrirá cómo utilizar estos pasos para preparar, constr
Dibuje un diagrama de flujos que refleje los pasos de practicante de ML. ¿Dónde te ves ahora mismo en el proceso? ¿Dónde predice que encontrará dificultades? ¿Qué te parece fácil?
-## [Cuestionario posterior a la conferencia](https://white-water-09ec41f0f.azurestaticapps.net/quiz/8?loc=es)
+## [Cuestionario posterior a la conferencia](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/8?loc=es)
## Revisión & Autoestudio
diff --git a/1-Introduction/4-techniques-of-ML/translations/README.id.md b/1-Introduction/4-techniques-of-ML/translations/README.id.md
index 37e0dab7bae199d474eea7a2b9bc835d81683391..47e7c5b8a64a91c0409824f9a6cc63dee690f3ed 100644
--- a/1-Introduction/4-techniques-of-ML/translations/README.id.md
+++ b/1-Introduction/4-techniques-of-ML/translations/README.id.md
@@ -5,7 +5,7 @@ Proses membangun, menggunakan, dan memelihara model machine learning dan data ya
- Memahami gambaran dari proses yang mendasari machine learning.
- Menjelajahi konsep dasar seperti '*models*', '*predictions*', dan '*training data*'.
-## [Quiz Pra-Pelajaran](https://white-water-09ec41f0f.azurestaticapps.net/quiz/7/)
+## [Quiz Pra-Pelajaran](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/7/)
## Pengantar
Gambaran membuat proses machine learning (ML) terdiri dari sejumlah langkah:
@@ -100,7 +100,7 @@ Dalam pelajaran ini, Kamu akan menemukan cara untuk menggunakan langkah-langkah
Gambarlah sebuah flow chart yang mencerminkan langkah-langkah seorang praktisi ML. Di mana kamu melihat diri kamu saat ini dalam prosesnya? Di mana kamu memprediksi kamu akan menemukan kesulitan? Apa yang tampak mudah bagi kamu?
-## [Quiz Pra-Pelajaran](https://white-water-09ec41f0f.azurestaticapps.net/quiz/8/)
+## [Quiz Pra-Pelajaran](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/8/)
## Ulasan & Belajar Mandiri
diff --git a/1-Introduction/4-techniques-of-ML/translations/README.it.md b/1-Introduction/4-techniques-of-ML/translations/README.it.md
index 0f7a4548a66004cf7214ba24fbe0dcc2dbb4a229..b6602f988926765c075fa2d29d6cab4b1bad1518 100644
--- a/1-Introduction/4-techniques-of-ML/translations/README.it.md
+++ b/1-Introduction/4-techniques-of-ML/translations/README.it.md
@@ -5,7 +5,7 @@ Il processo di creazione, utilizzo e mantenimento dei modelli di machine learnin
- Comprendere i processi ad alto livello alla base di machine learning.
- Esplorare concetti di base come "modelli", "previsioni" e "dati di addestramento".
-## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/7/?loc=it)
+## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/7/?loc=it)
## Introduzione
@@ -103,7 +103,7 @@ In queste lezioni si scoprirà come utilizzare questi passaggi per preparare, co
Disegnare un diagramma di flusso che rifletta i passaggi di un professionista di ML. Dove ci si vede in questo momento nel processo? Dove si prevede che sorgeranno difficoltà? Cosa sembra facile?
-## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/8/?loc=it)
+## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/8/?loc=it)
## Revisione e Auto Apprendimento
diff --git a/1-Introduction/4-techniques-of-ML/translations/README.ja.md b/1-Introduction/4-techniques-of-ML/translations/README.ja.md
index 8f30315bd9c88bd31746b0fb6f8b043a806d8228..e6880689dc4ffd1202c6941e3adb03229a6ef7cc 100644
--- a/1-Introduction/4-techniques-of-ML/translations/README.ja.md
+++ b/1-Introduction/4-techniques-of-ML/translations/README.ja.md
@@ -5,7 +5,7 @@
- 機械学習を支えるプロセスを高い水準で理解します。
- 「モデル」「予測」「訓練データ」などの基本的な概念を調べます。
-## [講義前の小テスト](https://white-water-09ec41f0f.azurestaticapps.net/quiz/7?loc=ja)
+## [講義前の小テスト](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/7?loc=ja)
## 導入
@@ -103,7 +103,7 @@
機械学習の学習者のステップを反映したフローチャートを描いてください。今の自分はこのプロセスのどこにいると思いますか？どこに困難があると予想しますか？あなたにとって簡単そうなことは何ですか？
-## [講義後の小テスト](https://white-water-09ec41f0f.azurestaticapps.net/quiz/8?loc=ja)
+## [講義後の小テスト](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/8?loc=ja)
## 振り返りと自主学習
diff --git a/1-Introduction/4-techniques-of-ML/translations/README.ko.md b/1-Introduction/4-techniques-of-ML/translations/README.ko.md
index 2ea9792858de72cf62673bffabb9f5a66efa114d..7126a3a42736fca15778fc5950d2247e1c346e67 100644
--- a/1-Introduction/4-techniques-of-ML/translations/README.ko.md
+++ b/1-Introduction/4-techniques-of-ML/translations/README.ko.md
@@ -5,7 +5,7 @@
- 머신러닝을 받쳐주는 프로세스를 고수준에서 이해합니다.
- 'models', 'predictions', 그리고 'training data'와 같은 기초 개념을 탐색합니다.
-## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/7/)
+## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/7/)
## 소개
@@ -103,7 +103,7 @@ feature는 데이터의 측정할 수 있는 속성입니다. 많은 데이터
ML 실무자의 단계를 반영한 플로우를 그려보세요. 프로세스에서 지금 어디에 있는 지 보이나요? 어려운 내용을 예상할 수 있나요? 어떤게 쉬울까요?
-## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/8/)
+## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/8/)
## 검토 & 자기주도 학습
diff --git a/1-Introduction/4-techniques-of-ML/translations/README.pt-br.md b/1-Introduction/4-techniques-of-ML/translations/README.pt-br.md
index 345f4e618137222e510957b6d114c9ba9585adfe..935dbe62d3d2f02464302194c4a89231b0ecbd66 100644
--- a/1-Introduction/4-techniques-of-ML/translations/README.pt-br.md
+++ b/1-Introduction/4-techniques-of-ML/translations/README.pt-br.md
@@ -5,7 +5,7 @@ O processo de construção, uso e manutenção de modelos de machine learning e
- Compreender os processos que sustentam o aprendizado de máquina em alto nível.
- Explorar conceitos básicos como 'modelos', 'previsões' e 'dados de treinamento'..
-## [Questionário pré-aula](https://white-water-09ec41f0f.azurestaticapps.net/quiz/7?loc=ptbr)
+## [Questionário pré-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/7?loc=ptbr)
## Introdução
@@ -103,7 +103,7 @@ Nessas lições, você descobrirá como usar essas etapas para preparar, criar,
Desenhe um fluxograma refletindo as etapas de um praticante de ML. Onde você se vê agora no processo? Onde você prevê que encontrará dificuldade? O que parece fácil para você?
-## [Questionário pós-aula](https://white-water-09ec41f0f.azurestaticapps.net/quiz/8?loc=ptbr)
+## [Questionário pós-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/8?loc=ptbr)
## Revisão e Autoestudo
diff --git a/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md b/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md
index f2b581ca933830e613baa20e43b8d231b7f4ff56..40f1e6693abdff1502b82e3e59159f6adeb222a6 100644
--- a/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md
+++ b/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md
@@ -6,7 +6,7 @@
- 在高层次上理解支持机器学习的过程。
- 探索基本概念，例如“模型”、“预测”和“训练数据”。
-## [课前测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/7/)
+## [课前测验](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/7/)
## 介绍
在较高的层次上，创建机器学习（ML）过程的工艺包括许多步骤：
@@ -101,7 +101,7 @@
画一个流程图，反映ML的步骤。在这个过程中，你认为自己现在在哪里？你预测你在哪里会遇到困难？什么对你来说很容易？
-## [阅读后测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/8/)
+## [阅读后测验](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/8/)
## 复习与自学
diff --git a/1-Introduction/4-techniques-of-ML/translations/README.zh-tw.md b/1-Introduction/4-techniques-of-ML/translations/README.zh-tw.md
index 7f7daf081281ff44130bdfe5fe33cc94205b5183..8d1222be1467a6ca6de96066420692c17b5c74f6 100644
--- a/1-Introduction/4-techniques-of-ML/translations/README.zh-tw.md
+++ b/1-Introduction/4-techniques-of-ML/translations/README.zh-tw.md
@@ -6,7 +6,7 @@
- 在高層次上理解支持機器學習的過程。
- 探索基本概念，例如「模型」、「預測」和「訓練數據」。
-## [課前測驗](https://white-water-09ec41f0f.azurestaticapps.net/quiz/7/)
+## [課前測驗](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/7/)
## 介紹
在較高的層次上，創建機器學習（ML）過程的工藝包括許多步驟：
@@ -100,7 +100,7 @@
畫一個流程圖，反映ML的步驟。在這個過程中，你認為自己現在在哪裏？你預測你在哪裏會遇到困難？什麽對你來說很容易？
-## [閱讀後測驗](https://white-water-09ec41f0f.azurestaticapps.net/quiz/8/)
+## [閱讀後測驗](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/8/)
## 復習與自學
diff --git a/2-Regression/1-Tools/README.md b/2-Regression/1-Tools/README.md
index ee7c72f5f93d312069c0c77960819292153e9336..fdd22b6997db96987762faef9f9fdeb99929f3f9 100644
--- a/2-Regression/1-Tools/README.md
+++ b/2-Regression/1-Tools/README.md
@@ -4,7 +4,7 @@
> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/9/)
+## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/9/)
> ### [This lesson is available in R!](./solution/R/lesson_1-R.ipynb)
@@ -199,7 +199,7 @@ Congratulations, you built your first linear regression model, created a predict
## 🚀Challenge
Plot a different variable from this dataset. Hint: edit this line: `X = X[:, np.newaxis, 2]`. Given this dataset's target, what are you able to discover about the progression of diabetes as a disease?
-## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/10/)
+## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/10/)
## Review & Self Study
diff --git a/2-Regression/1-Tools/translations/README.id.md b/2-Regression/1-Tools/translations/README.id.md
index cd30700c6b1cfaeff5070b12d409bfdf79473ba9..26cb6eee80563c22e2bdb6273154df9fc5519d56 100644
--- a/2-Regression/1-Tools/translations/README.id.md
+++ b/2-Regression/1-Tools/translations/README.id.md
@@ -4,7 +4,7 @@
> Catatan sketsa oleh [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [Kuis Pra-ceramah](https://white-water-09ec41f0f.azurestaticapps.net/quiz/9/)
+## [Kuis Pra-ceramah](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/9/)
## Pembukaan
Dalam keempat pelajaran ini, kamu akan belajar bagaimana membangun model regresi. Kita akan berdiskusi apa fungsi model tersebut dalam sejenak. Tetapi sebelum kamu melakukan apapun, pastikan bahwa kamu sudah mempunyai alat-alat yang diperlukan untuk memulai!
@@ -195,7 +195,7 @@ Selamat, kamu telah membangun model regresi linear pertamamu, membuat sebuah pre
## Tantangan
Gambarkan sebuah variabel yang beda dari *dataset* ini. Petunjuk: edit baris ini: `X = X[:, np.newaxis, 2]`. Mengetahui target *dataset* ini, apa yang kamu bisa menemukan tentang kemajuan diabetes sebagai sebuah penyakit?
-## [Kuis pasca-ceramah](https://white-water-09ec41f0f.azurestaticapps.net/quiz/10/)
+## [Kuis pasca-ceramah](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/10/)
## Review & Pembelajaran Mandiri
diff --git a/2-Regression/1-Tools/translations/README.it.md b/2-Regression/1-Tools/translations/README.it.md
index 25e726c2352183b76a8a37538f4a7863fae3585f..dba635930d0e8bcb34503cafc897d3369bd1e6f7 100644
--- a/2-Regression/1-Tools/translations/README.it.md
+++ b/2-Regression/1-Tools/translations/README.it.md
@@ -4,7 +4,7 @@
> Sketchnote di [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [Qui Pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/9/?loc=it)
+## [Qui Pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/9/?loc=it)
## Introduzione
@@ -197,7 +197,7 @@ Congratulazioni, si è costruito il primo modello di regressione lineare, creato
Tracciare una variabile diversa da questo insieme di dati. Suggerimento: modificare questa riga: `X = X[:, np.newaxis, 2]`. Dato l'obiettivo di questo insieme di dati, cosa si potrebbe riuscire a scoprire circa la progressione del diabete come matattia?
-## [Qui post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/10/?loc=it)
+## [Qui post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/10/?loc=it)
## Riepilogo e Auto Apprendimento
diff --git a/2-Regression/1-Tools/translations/README.ja.md b/2-Regression/1-Tools/translations/README.ja.md
index f7005978ad257b12895ccb7fc8bff87c754655b3..626f4714a51bf2f5229c3889b7c141e8875265ee 100644
--- a/2-Regression/1-Tools/translations/README.ja.md
+++ b/2-Regression/1-Tools/translations/README.ja.md
@@ -4,7 +4,7 @@
> [Tomomi Imura](https://www.twitter.com/girlie_mac) によって制作されたスケッチノート
-## [講義前クイズ](https://white-water-09ec41f0f.azurestaticapps.net/quiz/9?loc=ja)
+## [講義前クイズ](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/9?loc=ja)
## イントロダクション
@@ -205,7 +205,7 @@ Scikit-learnは、モデルを構築し、評価を行って実際に利用す
## 🚀チャレンジ
このデータセットから別の変数を選択してプロットしてください。ヒント： `X = X[:, np.newaxis, 2]` の行を編集する。今回のデータセットのターゲットである、糖尿病という病気の進行について、どのような発見があるのでしょうか？
-## [講義後クイズ](https://white-water-09ec41f0f.azurestaticapps.net/quiz/10?loc=ja)
+## [講義後クイズ](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/10?loc=ja)
## レビュー & 自主学習
diff --git a/2-Regression/1-Tools/translations/README.ko.md b/2-Regression/1-Tools/translations/README.ko.md
index 6ae3464394c41c89a8f71a1ffc9e75c2bcdcc16e..040401b0de21d5994858516643c75a4ed781ce35 100644
--- a/2-Regression/1-Tools/translations/README.ko.md
+++ b/2-Regression/1-Tools/translations/README.ko.md
@@ -4,7 +4,7 @@
> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/9/)
+## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/9/)
## 소개
@@ -200,7 +200,7 @@ Scikit-learn 사용하면 올바르게 모델을 만들고 사용하기 위해
이 데이터셋은 다른 변수를 Plot 합니다. 힌트: 이 라인을 수정합니다: `X = X[:, np.newaxis, 2]`. 이 데이터셋의 타겟이 주어질 때, 질병으로 당뇨가 진행되면 어떤 것을 탐색할 수 있나요?
-## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/10/)
+## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/10/)
## 검토 & 자기주도 학습
diff --git a/2-Regression/1-Tools/translations/README.pt-br.md b/2-Regression/1-Tools/translations/README.pt-br.md
index 1145b3343cca6724af976874399c8c066c62ca6c..fbbc77480670d699bd249e73fc96649b97848499 100644
--- a/2-Regression/1-Tools/translations/README.pt-br.md
+++ b/2-Regression/1-Tools/translations/README.pt-br.md
@@ -4,7 +4,7 @@
> _Sketchnote_ por [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [Questionário inicial](https://white-water-09ec41f0f.azurestaticapps.net/quiz/9?loc=ptbr)
+## [Questionário inicial](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/9?loc=ptbr)
> ### [Esta lição está disponível em R!](../solution/R/lesson_1-R.ipynb)
@@ -200,7 +200,7 @@ Parabéns, usando um conjunto de dados, você construiu seu primeiro modelo de r
## 🚀Desafio
Plote uma variável diferente desse mesmo conjunto de dados. Dica: edite a linha: `X = X[:, np.newaxis, 2]`. Dado o conjunto de dados alvo, o que pode ser descoberto sobre o progresso da diabetes como uma doença?
-## [Questionário para fixação](https://white-water-09ec41f0f.azurestaticapps.net/quiz/10?loc=ptbr)
+## [Questionário para fixação](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/10?loc=ptbr)
## Revisão e Auto Aprendizagem
diff --git a/2-Regression/1-Tools/translations/README.pt.md b/2-Regression/1-Tools/translations/README.pt.md
index 5635fed6516d2ae51cefe0ffa017bb06c7f4494e..bc18ee02be5a691286792dbc1b2ff25a0d1267cf 100644
--- a/2-Regression/1-Tools/translations/README.pt.md
+++ b/2-Regression/1-Tools/translations/README.pt.md
@@ -5,7 +5,7 @@
> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [Questionário pré-palestra](https://white-water-09ec41f0f.azurestaticapps.net/quiz/9/)
+## [Questionário pré-palestra](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/9/)
> ### [Esta lição está disponível em R!](./solution/R/lesson_1-R.ipynb)
@@ -202,7 +202,7 @@ Parabéns, construíste o teu primeiro modelo linear de regressão, criaste uma
## 🚀Challenge
Defina uma variável diferente deste conjunto de dados. Dica: edite esta linha:`X = X[:, np.newaxis, 2]`. Tendo em conta o objetivo deste conjunto de dados, o que é que consegue descobrir sobre a progressão da diabetes como uma doença?
-## [Questionário pós-palestra](https://white-water-09ec41f0f.azurestaticapps.net/quiz/10/)
+## [Questionário pós-palestra](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/10/)
## Review & Self Study
diff --git a/2-Regression/1-Tools/translations/README.tr.md b/2-Regression/1-Tools/translations/README.tr.md
index c391ee75fd3c93774e55171766f063d34a1111eb..561ab28bb5e8378225a975c755f359356b5da205 100644
--- a/2-Regression/1-Tools/translations/README.tr.md
+++ b/2-Regression/1-Tools/translations/README.tr.md
@@ -4,7 +4,7 @@
> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [Ders öncesi quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/9/)
+## [Ders öncesi quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/9/)
> ### [R dili ile bu dersin içeriği!](././solution/R/lesson_1-R.ipynb)
@@ -197,7 +197,7 @@ Tebrikler, ilk doğrusal regresyon modelinizi oluşturdunuz, onunla bir tahmin o
## 🚀Challenge
Bu veri kümesinden farklı bir değişken çizin. İpucu: bu satırı düzenleyin: `X = X[:, np.newaxis, 2]`. Bu veri setinin hedefi göz önüne alındığında, diyabetin bir hastalık olarak ilerlemesi hakkında neler keşfedebilirsiniz?
-## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/10/)
+## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/10/)
## İnceleme ve Bireysel Çalışma
diff --git a/2-Regression/1-Tools/translations/README.zh-cn.md b/2-Regression/1-Tools/translations/README.zh-cn.md
index 8f0b8c62d7c6f2823ba2c354da44c59e4e2c32ef..2fc162d6c26390d919840adade46e694d855ba84 100644
--- a/2-Regression/1-Tools/translations/README.zh-cn.md
+++ b/2-Regression/1-Tools/translations/README.zh-cn.md
@@ -4,7 +4,7 @@
> 作者 [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [课前测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/9/)
+## [课前测](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/9/)
## 介绍
在这四节课中，你将了解如何构建回归模型。我们将很快讨论这些是什么。但在你做任何事情之前，请确保你有合适的工具来开始这个过程！
@@ -194,7 +194,7 @@ Scikit-learn 使构建模型和评估它们的使用变得简单。它主要侧
从这个数据集中绘制一个不同的变量。提示：编辑这一行：`X = X[:, np.newaxis, 2]`。鉴于此数据集的目标，你能够发现糖尿病作为一种疾病的进展情况吗？
-## [课后测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/10/)
+## [课后测](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/10/)
## 复习与自学
diff --git a/2-Regression/1-Tools/translations/README.zh-tw.md b/2-Regression/1-Tools/translations/README.zh-tw.md
index 79b8aad1f1c657f334d40fc9f080da60fef90303..886aea2e61f0c9d22d3ba71658b4d7005ccfb8c0 100644
--- a/2-Regression/1-Tools/translations/README.zh-tw.md
+++ b/2-Regression/1-Tools/translations/README.zh-tw.md
@@ -4,7 +4,7 @@
> 作者 [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [課前測](https://white-water-09ec41f0f.azurestaticapps.net/quiz/9/)
+## [課前測](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/9/)
## 介紹
@@ -195,7 +195,7 @@ Scikit-learn 使構建模型和評估它們的使用變得簡單。它主要側
從這個數據集中繪製一個不同的變量。提示：編輯這一行：`X = X[:, np.newaxis, 2]`。鑒於此數據集的目標，你能夠發現糖尿病作為一種疾病的進展情況嗎？
-## [課後測](https://white-water-09ec41f0f.azurestaticapps.net/quiz/10/)
+## [課後測](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/10/)
## 復習與自學
diff --git a/2-Regression/2-Data/README.md b/2-Regression/2-Data/README.md
index 7c84166fad5fa65cb5e1bb71741f448ce673a857..939be63e6d16e99836a74768e15a35119752af77 100644
--- a/2-Regression/2-Data/README.md
+++ b/2-Regression/2-Data/README.md
@@ -4,7 +4,7 @@
Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/11/)
+## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/11/)
> ### [This lesson is available in R!](./solution/R/lesson_2-R.ipynb)
@@ -196,7 +196,7 @@ To get charts to display useful data, you usually need to group the data somehow
Explore the different types of visualization that Matplotlib offers. Which types are most appropriate for regression problems?
-## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/12/)
+## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/12/)
## Review & Self Study
diff --git a/2-Regression/2-Data/translations/README.es.md b/2-Regression/2-Data/translations/README.es.md
index 9ce762e820896d20264315ecde5b016c858051cf..2c188cfeafc3f0dd8d971e20870a608b2da15681 100644
--- a/2-Regression/2-Data/translations/README.es.md
+++ b/2-Regression/2-Data/translations/README.es.md
@@ -4,7 +4,7 @@
Infografía por [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Examen previo a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/11?loc=es)
+## [Examen previo a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/11?loc=es)
> ### [Esta lección se encuentra disponible en R!](../solution/R/lesson_2-R.ipynb)
@@ -196,7 +196,7 @@ Para obtener gráficas para mostrar datos útiles, necesitas agrupar los datos d
Explora los distintos tipos de visualización que ofrece Matplotlib. ¿Qué tipos son los más apropiados para problemas de regresión?
-## [Examen posterior a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/12?loc=es)
+## [Examen posterior a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/12?loc=es)
## Revisión y autoestudio
diff --git a/2-Regression/2-Data/translations/README.id.md b/2-Regression/2-Data/translations/README.id.md
index 9d8b5f18dadbe89ae1a6ad558f8a9e00c328c1d9..9e0f05d675174ed8e7145ea0edb7050b6693f190 100644
--- a/2-Regression/2-Data/translations/README.id.md
+++ b/2-Regression/2-Data/translations/README.id.md
@@ -3,7 +3,7 @@
> Infografik oleh [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Kuis pra-ceramah](https://white-water-09ec41f0f.azurestaticapps.net/quiz/11/)
+## [Kuis pra-ceramah](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/11/)
## Pembukaan
@@ -191,7 +191,7 @@ Untuk menjadikan sebuah grafik menjadi berguna, biasanya datanya harus dikelompo
Jelajahi jenis-jenis visualisasi yang beda dan yang disediakan Matplotlib. Jenis mana yang paling cocok untuk kasus regresi?
-## [Kuis pasca-ceramah](https://white-water-09ec41f0f.azurestaticapps.net/quiz/12/)
+## [Kuis pasca-ceramah](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/12/)
## Review & Pembelajaran Mandiri
diff --git a/2-Regression/2-Data/translations/README.it.md b/2-Regression/2-Data/translations/README.it.md
index b9882184a6abecd64f40abc84388e221bf852ec5..d0f51a57d161e63a0383ca83cb524488d989c91b 100644
--- a/2-Regression/2-Data/translations/README.it.md
+++ b/2-Regression/2-Data/translations/README.it.md
@@ -3,7 +3,7 @@
> 
> Infografica di [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/11/?loc=it)
+## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/11/?loc=it)
## Introduzione
@@ -190,7 +190,7 @@ Per fare in modo che i grafici mostrino dati utili, di solito è necessario ragg
Esplorare i diversi tipi di visualizzazione offerti da Matplotlib. Quali tipi sono più appropriati per i problemi di regressione?
-## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/12/?loc=it)
+## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/12/?loc=it)
## Revisione e Auto Apprendimento
diff --git a/2-Regression/2-Data/translations/README.ja.md b/2-Regression/2-Data/translations/README.ja.md
index ddd01a775e58cdedf8b98846b72e0dc3d3bc8282..a5f7dcf49cb8e1a999ac159818dfae2a5f017d01 100644
--- a/2-Regression/2-Data/translations/README.ja.md
+++ b/2-Regression/2-Data/translations/README.ja.md
@@ -4,7 +4,7 @@
>
> [Dasani Madipalli](https://twitter.com/dasani_decoded) によるインフォグラフィック
-## [講義前のクイズ](https://white-water-09ec41f0f.azurestaticapps.net/quiz/11?loc=ja)
+## [講義前のクイズ](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/11?loc=ja)
## イントロダクション
@@ -195,7 +195,7 @@ Jupyter notebookでうまく利用できるテータ可視化ライブラリの
Matplotlibが提供する様々なタイプのビジュアライゼーションを探ってみましょう。回帰の問題にはどのタイプが最も適しているでしょうか？
-## [講義後クイズ](https://white-water-09ec41f0f.azurestaticapps.net/quiz/12?loc=ja)
+## [講義後クイズ](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/12?loc=ja)
## レビュー & 自主学習
diff --git a/2-Regression/2-Data/translations/README.ko.md b/2-Regression/2-Data/translations/README.ko.md
index 64ddc721e1c918d34b3680546a017f4f53615a20..5bf393f7ffeb4419ae4bccf9f8f033a49fc77cf0 100644
--- a/2-Regression/2-Data/translations/README.ko.md
+++ b/2-Regression/2-Data/translations/README.ko.md
@@ -4,7 +4,7 @@
> Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/11/)
+## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/11/)
## 소개
@@ -191,7 +191,7 @@ Jupyter notebooks에서 잘 작동하는 데이터 시각화 라이브러리는
Matplotlib에서 제공하는 다양한 시각화 타입을 찾아보세요. regression 문제에 가장 적당한 타입은 무엇인가요?
-## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/12/)
+## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/12/)
## 검토 & 자기주도 학습
diff --git a/2-Regression/2-Data/translations/README.pt-br.md b/2-Regression/2-Data/translations/README.pt-br.md
index e5fdc94ca924e24d74766f20c03daba54bd73c44..7ba9fe859293b4f159fc65217afb8a215552ee4a 100644
--- a/2-Regression/2-Data/translations/README.pt-br.md
+++ b/2-Regression/2-Data/translations/README.pt-br.md
@@ -4,7 +4,7 @@
Infográfico por [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Questionário inicial](https://white-water-09ec41f0f.azurestaticapps.net/quiz/11?loc=ptbr)
+## [Questionário inicial](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/11?loc=ptbr)
> ### [Esta liçao está disponível em R!](../solution/R/lesson_2-R.ipynb)
@@ -197,7 +197,7 @@ Para fazer com que os gráficos exibam dados úteis, você precisa agrupar os da
Explore os diferentes tipos de visualização que o Matplotlib oferece. Quais tipos são mais adequados para problemas de regressão?
-## [Questionário para fixação](https://white-water-09ec41f0f.azurestaticapps.net/quiz/12?loc=ptbr)
+## [Questionário para fixação](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/12?loc=ptbr)
## Revisão e Auto Aprendizagem
diff --git a/2-Regression/2-Data/translations/README.pt.md b/2-Regression/2-Data/translations/README.pt.md
index 6cd25fad668fb1d6dfa1926a1d5a2a6cefc8b367..e2869a15d1d6e16d8b6dc156962d718a83b3db54 100644
--- a/2-Regression/2-Data/translations/README.pt.md
+++ b/2-Regression/2-Data/translations/README.pt.md
@@ -4,7 +4,7 @@
Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Teste de pré-aula](https://white-water-09ec41f0f.azurestaticapps.net/quiz/11/)
+## [Teste de pré-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/11/)
> ### [Esta lição está disponível em R!](./solution/R/lesson_2-R.ipynb)
@@ -196,7 +196,7 @@ Esta é uma visualização de dados mais útil! Parece indicar que o preço mais
Explore os diferentes tipos de visualização que o Matplotlib oferece. Que tipos são mais apropriados para problemas de regressão?
-## [Questionário pós-palestra](https://white-water-09ec41f0f.azurestaticapps.net/quiz/12/)
+## [Questionário pós-palestra](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/12/)
## Revisão e Estudo Automático
diff --git a/2-Regression/2-Data/translations/README.zh-cn.md b/2-Regression/2-Data/translations/README.zh-cn.md
index c31c872611bdf1a3adb7374ad8bef7022d128488..f204e1e71886f39596cfec923196f1af8c49869d 100644
--- a/2-Regression/2-Data/translations/README.zh-cn.md
+++ b/2-Regression/2-Data/translations/README.zh-cn.md
@@ -3,7 +3,7 @@
> 作者 [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [课前测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/11/)
+## [课前测](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/11/)
## 介绍
@@ -192,7 +192,7 @@
探索 Matplotlib 提供的不同类型的可视化。哪种类型最适合回归问题？
-## [课后测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/12/)
+## [课后测](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/12/)
## 复习与自学
diff --git a/2-Regression/2-Data/translations/README.zh-tw.md b/2-Regression/2-Data/translations/README.zh-tw.md
index c3d92fde0692bc678412ac0f7903c83a14aec51f..f9bfbb9907105daff0aabd940deb10e1d8c34700 100644
--- a/2-Regression/2-Data/translations/README.zh-tw.md
+++ b/2-Regression/2-Data/translations/README.zh-tw.md
@@ -3,7 +3,7 @@
> 作者 [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [課前測](https://white-water-09ec41f0f.azurestaticapps.net/quiz/11/)
+## [課前測](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/11/)
## 介紹
@@ -192,7 +192,7 @@
探索 Matplotlib 提供的不同類型的可視化。哪種類型最適合回歸問題？
-## [課後測](https://white-water-09ec41f0f.azurestaticapps.net/quiz/12/)
+## [課後測](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/12/)
## 復習與自學
diff --git a/2-Regression/3-Linear/README.md b/2-Regression/3-Linear/README.md
index 40a07fce20cb640d6291c707c64ee4fd04bc6db0..b7e07bac7e2e441b05f37b885012de9f534974d1 100644
--- a/2-Regression/3-Linear/README.md
+++ b/2-Regression/3-Linear/README.md
@@ -2,7 +2,7 @@
> Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/13/)
+## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/13/)
> ### [This lesson is available in R!](./solution/R/lesson_3-R.ipynb)
### Introduction
@@ -326,7 +326,7 @@ This should give us the best determination coefficient of almost 97%, and MSE=2.
Test several different variables in this notebook to see how correlation corresponds to model accuracy.
-## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14/)
+## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/14/)
## Review & Self Study
diff --git a/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb b/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb
index 4580481d3b85bfef7148f21ac15f6b3d8af972ae..ba156fb61f811abb842a95d895fa12d17a44c42b 100644
--- a/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb
+++ b/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb
@@ -1058,7 +1058,7 @@
"\n",
"Test several different variables in this notebook to see how correlation corresponds to model accuracy.\n",
"\n",
- "## [**Post-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14/)\n",
+ "## [**Post-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/14/)\n",
"\n",
"## **Review & Self Study**\n",
"\n",
diff --git a/2-Regression/3-Linear/solution/R/lesson_3.Rmd b/2-Regression/3-Linear/solution/R/lesson_3.Rmd
index 7997b0d8f3d0d63575db18822129202e3f6fe3e3..712011f488c2f388a1adcc7a49887a5b9731e114 100644
--- a/2-Regression/3-Linear/solution/R/lesson_3.Rmd
+++ b/2-Regression/3-Linear/solution/R/lesson_3.Rmd
@@ -662,7 +662,7 @@ The `polynomial model` prediction does make sense, given the scatter plots of `p
Test several different variables in this notebook to see how correlation corresponds to model accuracy.
-## [**Post-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14/)
+## [**Post-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/14/)
## **Review & Self Study**
diff --git a/2-Regression/3-Linear/translations/README.es.md b/2-Regression/3-Linear/translations/README.es.md
index c76aec955692fc2193823e9477ed597a10a1e903..db79c5af5354e34dd254c466b44d30ebc9bb5267 100644
--- a/2-Regression/3-Linear/translations/README.es.md
+++ b/2-Regression/3-Linear/translations/README.es.md
@@ -3,7 +3,7 @@
> Infografía de [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Examen previo a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/13?loc=es)
+## [Examen previo a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/13?loc=es)
> ### [¡Esta lección está disponible en R!](../solution/R/lesson_3-R.ipynb)
@@ -331,7 +331,7 @@ Llama a `predict()` para hacer una predicción:
Prueba variables diferentes en este notebook para ver cómo la correlación corresponde a la precisión del modelo.
-## [Examen posterior a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14?loc=es)
+## [Examen posterior a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/14?loc=es)
## Revisión y auto-estudio
diff --git a/2-Regression/3-Linear/translations/README.id.md b/2-Regression/3-Linear/translations/README.id.md
index ce2ee4098aaeddc9a1f2db39b33668246aa4f348..b454c4db78ebb74efd0f64ec189a24fbe49c0ffc 100644
--- a/2-Regression/3-Linear/translations/README.id.md
+++ b/2-Regression/3-Linear/translations/README.id.md
@@ -2,7 +2,7 @@
> Infografik oleh [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Kuis pra-ceramah](https://white-water-09ec41f0f.azurestaticapps.net/quiz/13/)
+## [Kuis pra-ceramah](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/13/)
### Pembukaan
Selama ini kamu telah menjelajahi apa regresi itu dengan data contoh yang dikumpulkan dari *dataset* harga labu yang kita akan gunakan terus sepanjang pelajaran ini. Kamu juga telah memvisualisasikannya dengan Matplotlib.
@@ -324,7 +324,7 @@ Itu sangat masuk akal dengan bagan sebelumnya! Selain itu, jika ini model lebih
Coba-cobalah variabel-variabel yang lain di *notebook* ini untuk melihat bagaimana korelasi berhubungan dengan akurasi model.
-## [Kuis pasca-ceramah](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14/)
+## [Kuis pasca-ceramah](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/14/)
## Review & Pembelajaran Mandiri
diff --git a/2-Regression/3-Linear/translations/README.it.md b/2-Regression/3-Linear/translations/README.it.md
index a95d005e43f4626999e9b67c85586f384c1c6686..f2c0a29501f45309ef495561a9f424dac08a883a 100644
--- a/2-Regression/3-Linear/translations/README.it.md
+++ b/2-Regression/3-Linear/translations/README.it.md
@@ -3,7 +3,7 @@
> Infografica di [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/13/?loc=it)
+## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/13/?loc=it)
### Introduzione
@@ -328,7 +328,7 @@ Ben fatto! Sono stati creati due modelli di regressione in una lezione. Nella s
Testare diverse variabili in questo notebook per vedere come la correlazione corrisponde all'accuratezza del modello.
-## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14/?loc=it)
+## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/14/?loc=it)
## Revisione e Auto Apprendimento
diff --git a/2-Regression/3-Linear/translations/README.ja.md b/2-Regression/3-Linear/translations/README.ja.md
index 2dbc0f3219e5cbcd5843a4f24fc835477ca30384..0bebdb969f92da31715ee30d6fdd5a59c05067bb 100644
--- a/2-Regression/3-Linear/translations/README.ja.md
+++ b/2-Regression/3-Linear/translations/README.ja.md
@@ -2,7 +2,7 @@
> [Dasani Madipalli](https://twitter.com/dasani_decoded) によるインフォグラフィック
-## [講義前のクイズ](https://white-water-09ec41f0f.azurestaticapps.net/quiz/13/)
+## [講義前のクイズ](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/13/)
### イントロダクション
これまで、このレッスンで使用するカボチャの価格データセットから集めたサンプルデータを使って、回帰とは何かを探ってきました。また、Matplotlibを使って可視化を行いました。
@@ -323,7 +323,7 @@ Scikit-learnには、多項式回帰モデルを構築するための便利なAP
このノートブックでいくつかの異なる変数をテストし、相関関係がモデルの精度にどのように影響するかを確認してみてください。
-## [講義後クイズ](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14/)
+## [講義後クイズ](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/14/)
## レビュー & 自主学習
diff --git a/2-Regression/3-Linear/translations/README.ko.md b/2-Regression/3-Linear/translations/README.ko.md
index 57ba3201dedd536900668f84f3a39797ceadb860..baa9abf899cc86c48e93a1d23be814c257e54fc7 100644
--- a/2-Regression/3-Linear/translations/README.ko.md
+++ b/2-Regression/3-Linear/translations/README.ko.md
@@ -3,7 +3,7 @@
> Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/13/)
+## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/13/)
### 소개
@@ -327,7 +327,7 @@ Scikit-learn에는 polynomial regression 모델을 만들 때 도움을 받을
노트북에서 다른 변수를 테스트하면서 상관 관계가 모델 정확도에 어떻게 대응되는 지 봅니다.
-## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14/)
+## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/14/)
## 검토 & 자기주도 학습
diff --git a/2-Regression/3-Linear/translations/README.pt-br.md b/2-Regression/3-Linear/translations/README.pt-br.md
index a0a5c46c8fd59fb04a5ba76f64dc03347a54350c..81b137c7bfe517309b747686b7132f7871460737 100644
--- a/2-Regression/3-Linear/translations/README.pt-br.md
+++ b/2-Regression/3-Linear/translations/README.pt-br.md
@@ -3,7 +3,7 @@
> Infográfico por [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Questionário inicial](https://white-water-09ec41f0f.azurestaticapps.net/quiz/13?loc=ptbr)
+## [Questionário inicial](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/13?loc=ptbr)
> ### [Esta liçao está disponível em R!](../solution/R/lesson_3-R.ipynb)
@@ -331,7 +331,7 @@ E se esse modelo for melhor que o anterior usando o mesmo conjunto de dados, voc
Teste variáveis diferentes neste _notebook_ para ver como a correlação corresponde à acurácia do modelo.
-## [Questionário para fixação](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14?loc=ptbr)
+## [Questionário para fixação](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/14?loc=ptbr)
## Revisão e Auto Aprendizagem
diff --git a/2-Regression/3-Linear/translations/README.pt.md b/2-Regression/3-Linear/translations/README.pt.md
index c3386d96c3d4c3ee276dc6fe4f696e1ddb8d9065..68f6271484885fa321169f3c8853db7ec20df2c0 100644
--- a/2-Regression/3-Linear/translations/README.pt.md
+++ b/2-Regression/3-Linear/translations/README.pt.md
@@ -2,7 +2,7 @@
> Infográfico de [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Questionário pré-seleção](https://white-water-09ec41f0f.azurestaticapps.net/quiz/13/)
+## [Questionário pré-seleção](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/13/)
> ### [Esta lição está disponível em R!](./solution/R/lesson_3-R.ipynb)
### Introdução
@@ -321,7 +321,7 @@ Faz sentido, dado o enredo! E, se este é um modelo melhor do que o anterior, ol
## 🚀 desafio
Teste várias variáveis diferentes neste bloco de notas para ver como a correlação corresponde à precisão do modelo.
-##[Questionário pós-palestra](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14/)
+##[Questionário pós-palestra](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/14/)
## Revisão e Estudo Automático
diff --git a/2-Regression/3-Linear/translations/README.zh-cn.md b/2-Regression/3-Linear/translations/README.zh-cn.md
index bffa0e22d7a2843896ab2f978b946991316286ea..94e595455f422bfb068d08cd10fc60ad45aa2c07 100644
--- a/2-Regression/3-Linear/translations/README.zh-cn.md
+++ b/2-Regression/3-Linear/translations/README.zh-cn.md
@@ -3,7 +3,7 @@
> 作者 [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [课前测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/13/)
+## [课前测](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/13/)
### 介绍
@@ -330,7 +330,7 @@ Scikit-learn 包含一个用于构建多项式回归模型的有用 API - `make_
在此 notebook 中测试几个不同的变量，以查看相关性与模型准确性的对应关系。
-## [课后测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14/)
+## [课后测](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/14/)
## 复习与自学
diff --git a/2-Regression/3-Linear/translations/README.zh-tw.md b/2-Regression/3-Linear/translations/README.zh-tw.md
index a5a78ed4edb72925b08dd0c8cd15e93e3f4a39af..4592c2f11b9310d8c095b4bd205abf38aed98dea 100644
--- a/2-Regression/3-Linear/translations/README.zh-tw.md
+++ b/2-Regression/3-Linear/translations/README.zh-tw.md
@@ -4,7 +4,7 @@
> 作者 [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [課前測](https://white-water-09ec41f0f.azurestaticapps.net/quiz/13/)
+## [課前測](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/13/)
### 介紹
@@ -331,7 +331,7 @@ Scikit-learn 包含一個用於構建多項式回歸模型的有用 API - `make_
在此 notebook 中測試幾個不同的變量，以查看相關性與模型準確性的對應關系。
-## [課後測](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14/)
+## [課後測](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/14/)
## 復習與自學
diff --git a/2-Regression/4-Logistic/README.md b/2-Regression/4-Logistic/README.md
index 9ff52164e67f28907e92a4dfe63cfd574faf923d..6da9491572b6a6197acc0cf288abaf44510589e1 100644
--- a/2-Regression/4-Logistic/README.md
+++ b/2-Regression/4-Logistic/README.md
@@ -2,7 +2,7 @@
> Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/)
+## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/15/)
> ### [This lesson is available in R!](./solution/R/lesson_4-R.ipynb)
@@ -298,7 +298,7 @@ In future lessons on classifications, you will learn how to iterate to improve y
There's a lot more to unpack regarding logistic regression! But the best way to learn is to experiment. Find a dataset that lends itself to this type of analysis and build a model with it. What do you learn? tip: try [Kaggle](https://www.kaggle.com/search?q=logistic+regression+datasets) for interesting datasets.
-## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/16/)
+## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/16/)
## Review & Self Study
diff --git a/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb b/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb
index 25320f05f539f0e8e31039724425f54af32bb557..c82e79f153553d2de27a71ae600346a151b37c2c 100644
--- a/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb
+++ b/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb
@@ -45,7 +45,7 @@
{
"cell_type": "markdown",
"source": [
- "#### ** [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/)**\n",
+ "#### ** [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/15/)**\n",
"\n",
"#### Introduction\n",
"\n",
diff --git a/2-Regression/4-Logistic/solution/R/lesson_4.Rmd b/2-Regression/4-Logistic/solution/R/lesson_4.Rmd
index 0eeadaf3609b0ee26e5815c4d09f94bca4d1f657..26ac170ce7065e21593369ed6f9f12075ea80a77 100644
--- a/2-Regression/4-Logistic/solution/R/lesson_4.Rmd
+++ b/2-Regression/4-Logistic/solution/R/lesson_4.Rmd
@@ -14,7 +14,7 @@ output:
{width="600"}
-#### ** [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/)**
+#### ** [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/15/)**
#### Introduction
diff --git a/2-Regression/4-Logistic/translations/README.es.md b/2-Regression/4-Logistic/translations/README.es.md
index e66ef072794a0b8b3d60bf1637f73b2232943b2f..d0e9913d136078ee0f7d5c5f8919d363419ede1f 100644
--- a/2-Regression/4-Logistic/translations/README.es.md
+++ b/2-Regression/4-Logistic/translations/README.es.md
@@ -3,7 +3,7 @@
> Infografía de [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Examen previo a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15?loc=es)
+## [Examen previo a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/15?loc=es)
> ### [Esta lección se encuentra disponible en R!](../solution/R/lesson_4-R.ipynb)
@@ -302,7 +302,7 @@ En futuras lecciones de clasificación, aprenderás cómo iterar para mejorar lo
¡Hay mucho más para desempacar respecto a la regresión logística! Pero la mejor forma de aprender es experimentar. Encuentra un conjunto de datos que se preste para este tipo de análisis y construye un modelo con él. ¿Qué aprendes? tipo: prueba [Kaggle](https://www.kaggle.com/search?q=logistic+regression+datasets) por conjuntos de datos interesantes.
-## [Examen posterior a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/16?loc=es)
+## [Examen posterior a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/16?loc=es)
## Revisión & autoestudio
diff --git a/2-Regression/4-Logistic/translations/README.id.md b/2-Regression/4-Logistic/translations/README.id.md
index 553205d71288b73b1423c26d09da7e8f7b078826..5ec38e2d9fb111bff154f762ac19a6305cee658d 100644
--- a/2-Regression/4-Logistic/translations/README.id.md
+++ b/2-Regression/4-Logistic/translations/README.id.md
@@ -3,7 +3,7 @@
> Infografik oleh [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Kuis pra-ceramah](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/)
+## [Kuis pra-ceramah](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/15/)
## Pembukaan
@@ -291,7 +291,7 @@ Nanti dalam pelajaran lebih lanjut tentang klasifikasi, kamu akan belajar bagaim
Masih ada banyak tentang regresi logistik! Tetapi cara paling baik adalah untuk bereksperimen. Carilah sebuah *dataset* yang bisa diteliti seperti ini dan bangunlah sebuah model darinya. Apa yang kamu pelajari? Petunjuk: Coba [Kaggle](https://kaggle.com) untuk *dataset-dataset* menarik.
-## [Kuis pasca-ceramah](https://white-water-09ec41f0f.azurestaticapps.net/quiz/16/)
+## [Kuis pasca-ceramah](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/16/)
## Review & Pembelajaran mandiri
diff --git a/2-Regression/4-Logistic/translations/README.it.md b/2-Regression/4-Logistic/translations/README.it.md
index 943a68c78409d5d35520b756ff929dd9d2bb7a67..c4e269794fe543b2c4ad09dc1be3b5aa8335c744 100644
--- a/2-Regression/4-Logistic/translations/README.it.md
+++ b/2-Regression/4-Logistic/translations/README.it.md
@@ -3,7 +3,7 @@
> Infografica di [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/?loc=it)
+## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/15/?loc=it)
## Introduzione
@@ -284,7 +284,7 @@ Nelle lezioni future sulle classificazioni si imparerà come eseguire l'iterazio
C'è molto altro da svelare riguardo alla regressione logistica! Ma il modo migliore per imparare è sperimentare. Trovare un insieme di dati che si presti a questo tipo di analisi e costruire un modello con esso. Cosa si è appreso? suggerimento: provare [Kaggle](https://kaggle.com) per ottenere insiemi di dati interessanti.
-## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/16/?loc=it)
+## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/16/?loc=it)
## Revisione e Auto Apprendimento
diff --git a/2-Regression/4-Logistic/translations/README.ja.md b/2-Regression/4-Logistic/translations/README.ja.md
index 662a1eafba82c8c58b761c3fbed78a5eb7056a47..0158d9ed0533f5f1d827f2cef682ee691126e526 100644
--- a/2-Regression/4-Logistic/translations/README.ja.md
+++ b/2-Regression/4-Logistic/translations/README.ja.md
@@ -2,7 +2,7 @@
> [Dasani Madipalli](https://twitter.com/dasani_decoded) によるインフォグラフィック
-## [講義前のクイズ](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/)
+## [講義前のクイズ](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/15/)
## イントロダクション
@@ -299,7 +299,7 @@ print(auc)
ロジスティック回帰については、まだまだ解き明かすべきことがたくさんあります。しかし、学ぶための最良の方法は、実験することです。この種の分析に適したデータセットを見つけて、それを使ってモデルを構築してみましょう。ヒント：面白いデータセットを探すために[Kaggle](https://www.kaggle.com/search?q=logistic+regression+datasets) を試してみてください。
-## [講義後クイズ](https://white-water-09ec41f0f.azurestaticapps.net/quiz/16/)
+## [講義後クイズ](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/16/)
## レビュー & 自主学習
diff --git a/2-Regression/4-Logistic/translations/README.ko.md b/2-Regression/4-Logistic/translations/README.ko.md
index 1bca89620906da97f91a34362b3117349a853a3d..5a6ac4a3dd24166bdfa563aa3773a825613a3f22 100644
--- a/2-Regression/4-Logistic/translations/README.ko.md
+++ b/2-Regression/4-Logistic/translations/README.ko.md
@@ -3,7 +3,7 @@
> Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/)
+## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/15/)
## 소개
@@ -300,7 +300,7 @@ classifications에 대한 이후 강의에서, 모델의 스코어를 개선하
logistic regression과 관련해서 풀어야할 내용이 더 있습니다! 하지만 배우기 좋은 방식은 실험입니다. 이런 분석에 적당한 데이터셋을 찾아서 모델을 만듭니다. 무엇을 배우나요? 팁: 흥미로운 데이터셋으로 [Kaggle](https://www.kaggle.com/search?q=logistic+regression+datasets)에서 시도해보세요.
-## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/16/)
+## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/16/)
## 검토 & 자기주도 학습
diff --git a/2-Regression/4-Logistic/translations/README.pt-br.md b/2-Regression/4-Logistic/translations/README.pt-br.md
index 3633bc105ce2889b2500ecd920ebff6f893f9cfd..b62f4a33f91b7252bbb261ce6a9b375479ef32ad 100644
--- a/2-Regression/4-Logistic/translations/README.pt-br.md
+++ b/2-Regression/4-Logistic/translations/README.pt-br.md
@@ -2,7 +2,7 @@
> Infográfico por [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Questionário inicial](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15?loc=ptbr)
+## [Questionário inicial](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/15?loc=ptbr)
> ### [Esta liçao está disponível em R!](../solution/R/lesson_4-R.ipynb)
@@ -300,7 +300,7 @@ Em outras lições sobre classificação, você aprenderá como iterar para melh
Ainda há muito sobre regressão logística! E a melhor maneira de aprender é experimentando. Encontre um conjunto de dados para este tipo de análise e construa um modelo com ele. O que você aprendeu? dica: tente o [Kaggle](https://www.kaggle.com/search?q=logistic+regression+datasets) para conjuntos de dados interessantes.
-## [Questionário para fixação](https://white-water-09ec41f0f.azurestaticapps.net/quiz/16?loc=ptbr)
+## [Questionário para fixação](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/16?loc=ptbr)
## Revisão e Auto Aprendizagem
diff --git a/2-Regression/4-Logistic/translations/README.pt.md b/2-Regression/4-Logistic/translations/README.pt.md
index 2eae5c0620e863a20c64e313ecec49a725b37b0d..82929a2310403c95b36fe7e110ee893e3d24d773 100644
--- a/2-Regression/4-Logistic/translations/README.pt.md
+++ b/2-Regression/4-Logistic/translations/README.pt.md
@@ -2,7 +2,7 @@
> Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Questionário pré-palestra](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/)
+## [Questionário pré-palestra](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/15/)
> ### [Esta lição está disponível em R!](./solution/R/lesson_4-R.ipynb)
@@ -291,7 +291,7 @@ Em lições futuras sobre classificações, você aprenderá a iterar para melho
Há muito mais a desempacotar em relação à regressão logística! Mas a melhor maneira de aprender é experimentar. Encontre um conjunto de dados que se preste a esse tipo de análise e construa um modelo com ele. O que você aprende? dica: tente [Kaggle](https://www.kaggle.com/search?q=logistic+regression+datasets) para obter conjuntos de dados interessantes.
-## [Teste pós-aula](https://white-water-09ec41f0f.azurestaticapps.net/quiz/16/)
+## [Teste pós-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/16/)
## Análise e autoestudo
diff --git a/2-Regression/4-Logistic/translations/README.zh-cn.md b/2-Regression/4-Logistic/translations/README.zh-cn.md
index 44994663f8031b249a514cdb940dfa843cc057f4..fb80b284a12ed1bea73324e0ecac1c3b13b74d6c 100644
--- a/2-Regression/4-Logistic/translations/README.zh-cn.md
+++ b/2-Regression/4-Logistic/translations/README.zh-cn.md
@@ -3,7 +3,7 @@
> 作者 [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [课前测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/)
+## [课前测](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/15/)
## 介绍
@@ -289,7 +289,7 @@ print(auc)
关于逻辑回归，还有很多东西需要解开！但最好的学习方法是实验。找到适合此类分析的数据集并用它构建模型。你学到了什么？小贴士：尝试 [Kaggle](https://kaggle.com) 获取有趣的数据集。
-## [课后测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/16/)
+## [课后测](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/16/)
## 复习与自学
diff --git a/2-Regression/4-Logistic/translations/README.zh-tw.md b/2-Regression/4-Logistic/translations/README.zh-tw.md
index b8789d350df944b7bf75ab177aaed61b6dc06517..ab4810988ceb076eb031d82a6197103708491812 100644
--- a/2-Regression/4-Logistic/translations/README.zh-tw.md
+++ b/2-Regression/4-Logistic/translations/README.zh-tw.md
@@ -4,7 +4,7 @@
> 作者 [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [課前測](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/)
+## [課前測](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/15/)
## 介紹
@@ -290,7 +290,7 @@ print(auc)
關於邏輯回歸，還有很多東西需要解開！但最好的學習方法是實驗。找到適合此類分析的數據集並用它構建模型。你學到了什麽？小貼士：嘗試 [Kaggle](https://kaggle.com) 獲取有趣的數據集。
-## [課後測](https://white-water-09ec41f0f.azurestaticapps.net/quiz/16/)
+## [課後測](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/16/)
## 復習與自學
diff --git a/3-Web-App/1-Web-App/README.md b/3-Web-App/1-Web-App/README.md
index 5af9f65024899aa0f22cf168960f0244e00ae0e5..7af22fca3529d022352d82d7e6fe867e1a5d740e 100644
--- a/3-Web-App/1-Web-App/README.md
+++ b/3-Web-App/1-Web-App/README.md
@@ -11,7 +11,7 @@ We will continue our use of notebooks to clean data and train our model, but you
To do this, you need to build a web app using Flask.
-## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/17/)
+## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/17/)
## Building an app
@@ -334,7 +334,7 @@ In a professional setting, you can see how good communication is necessary betwe
Instead of working in a notebook and importing the model to the Flask app, you could train the model right within the Flask app! Try converting your Python code in the notebook, perhaps after your data is cleaned, to train the model from within the app on a route called `train`. What are the pros and cons of pursuing this method?
-## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/18/)
+## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/18/)
## Review & Self Study
diff --git a/3-Web-App/1-Web-App/translations/README.es.md b/3-Web-App/1-Web-App/translations/README.es.md
index 5a825b7022462eba622af447589fbbceb755a236..5eb396b437209c7e6f0f6df3af42a0d102082ef0 100644
--- a/3-Web-App/1-Web-App/translations/README.es.md
+++ b/3-Web-App/1-Web-App/translations/README.es.md
@@ -11,7 +11,7 @@ Continuaremos nuestro uso de notebooks para limpiar los datos y entrenar nuestro
Para hacer esto, necesitas construir una aplicación web usando Flask.
-## [Examen previo a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/17?loc=es)
+## [Examen previo a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/17?loc=es)
## Construyendo una aplicación
@@ -335,7 +335,7 @@ En un entorno profesional, puedes ver cómo la buena comunicación es necesaria
En lugar de trabajar en un notebook e importar el modelo a una aplicación Flask, ¡podrías entrenar el modelo directo en la aplicación Flask! Intenta convertir tu código Python en el notebook, quizá después que tus datos sean limpiados, para entrenar el modelo desde la aplicación en una ruta llamada `train`. ¿Cuáles son los pros y contras de seguir este método?
-## [Examen posterior a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/18?loc=es)
+## [Examen posterior a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/18?loc=es)
## Revisión y autoestudio
diff --git a/3-Web-App/1-Web-App/translations/README.it.md b/3-Web-App/1-Web-App/translations/README.it.md
index 9d5fa430fdbe4b9f6b1088d1f0770617e5a39ea3..fec92decf36aeeea2e3a6d326402e7df034ae485 100644
--- a/3-Web-App/1-Web-App/translations/README.it.md
+++ b/3-Web-App/1-Web-App/translations/README.it.md
@@ -11,7 +11,7 @@ Si continuerà a utilizzare il notebook per pulire i dati e addestrare il modell
Per fare ciò, è necessario creare un'app Web utilizzando Flask.
-## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/17/?loc=it)
+## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/17/?loc=it)
## Costruire un'app
@@ -334,7 +334,7 @@ In un ambiente professionale, si può vedere quanto sia necessaria una buona com
Invece di lavorare su un notebook e importare il modello nell'app Flask, si può addestrare il modello direttamente nell'app Flask! Provare a convertire il codice Python nel notebook, magari dopo che i dati sono stati puliti, per addestrare il modello dall'interno dell'app su un percorso chiamato `/train`. Quali sono i pro e i contro nel seguire questo metodo?
-## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/18/?loc=it)
+## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/18/?loc=it)
## Revisione e Auto Apprendimento
diff --git a/3-Web-App/1-Web-App/translations/README.ja.md b/3-Web-App/1-Web-App/translations/README.ja.md
index ba9f917086e1c109a1329ef0c420eb11be133d51..7d18f6bf48386c9db8f286f6043bbac0ac5b1fa2 100644
--- a/3-Web-App/1-Web-App/translations/README.ja.md
+++ b/3-Web-App/1-Web-App/translations/README.ja.md
@@ -11,7 +11,7 @@
そのためには、Flaskを使ってWebアプリを構築する必要があります。
-## [講義前の小テスト](https://white-water-09ec41f0f.azurestaticapps.net/quiz/17?loc=ja)
+## [講義前の小テスト](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/17?loc=ja)
## アプリの構築
@@ -334,7 +334,7 @@ print(model.predict([[50,44,-12]]))
ノートブックで作業してモデルをFlaskアプリにインポートする代わりに、Flaskアプリの中でモデルをトレーニングすることができます。おそらくデータをクリーニングした後になりますが、ノートブック内のPythonコードを変換して、アプリ内の `train` というパスでモデルを学習してみてください。この方法を採用することの長所と短所は何でしょうか？
-## [講義後の小テスト](https://white-water-09ec41f0f.azurestaticapps.net/quiz/18?loc=ja)
+## [講義後の小テスト](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/18?loc=ja)
## 振り返りと自主学習
diff --git a/3-Web-App/1-Web-App/translations/README.ko.md b/3-Web-App/1-Web-App/translations/README.ko.md
index 24330063a54cc7c0ca5320dcf1665481c1bfbf4e..a25e5be6e316dfec37c3653357ff1f5e71de0abe 100644
--- a/3-Web-App/1-Web-App/translations/README.ko.md
+++ b/3-Web-App/1-Web-App/translations/README.ko.md
@@ -11,7 +11,7 @@
이러면, Flask로 웹 앱을 만들어야 합니다.
-## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/17/)
+## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/17/)
## 앱 만들기
@@ -335,7 +335,7 @@ Flask와 pickled 모델과 같이, 모델을 사용하는 이 방식은, 비교
노트북에서 작성하고 Flask 앱에서 모델을 가져오는 대신, Flask 앱에서 바로 모델을 훈련할 수 있습니다! 어쩌면 데이터를 정리하고, 노트북에서 Python 코드로 변환해서, `train`이라고 불리는 라우터로 앱에서 모델을 훈련합니다. 이러한 방식을 추구했을 때 장점과 단점은 무엇인가요?
-## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/18/)
+## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/18/)
## 검토 & 자기주도 학습
diff --git a/3-Web-App/1-Web-App/translations/README.pt-br.md b/3-Web-App/1-Web-App/translations/README.pt-br.md
index 0d7446291caa53357a59c0153e025add90ccacda..7fdf3a7f9e5274af063b00f180b360cd608951a9 100644
--- a/3-Web-App/1-Web-App/translations/README.pt-br.md
+++ b/3-Web-App/1-Web-App/translations/README.pt-br.md
@@ -11,7 +11,7 @@ Continuaremos nosso uso de notebooks para limpar dados e treinar nosso modelo, m
Para fazer isso, você precisa construir um aplicativo da web usando o Flask.
-## [Teste pré-aula](https://white-water-09ec41f0f.azurestaticapps.net/quiz/17?loc=ptbr)
+## [Teste pré-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/17?loc=ptbr)
## Construindo um aplicativo
@@ -337,7 +337,7 @@ Em um ambiente profissional, você pode ver como uma boa comunicação é necess
Em vez de trabalhar em um notebook e importar o modelo para o aplicativo Flask, você pode treinar o modelo diretamente no aplicativo Flask! Tente converter seu código Python no notebook, talvez depois que seus dados forem limpos, para treinar o modelo de dentro do aplicativo em uma rota chamada `train`. Quais são os prós e os contras de seguir esse método?
-## [Teste pós-aula](https://white-water-09ec41f0f.azurestaticapps.net/quiz/18?loc=ptbr)
+## [Teste pós-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/18?loc=ptbr)
## Revisão e autoestudo
diff --git a/3-Web-App/1-Web-App/translations/README.pt.md b/3-Web-App/1-Web-App/translations/README.pt.md
index 00b18c026853b760a60302ca84e2bf0a0da5832c..92c02107c8b11ac7d9db956c5b7051c4cc0e4440 100644
--- a/3-Web-App/1-Web-App/translations/README.pt.md
+++ b/3-Web-App/1-Web-App/translations/README.pt.md
@@ -11,7 +11,7 @@ Continuaremos a usar notebooks para limpar dados e treinar nosso modelo, mas voc
Para fazer isso, você precisa construir um aplicativo Web usando Flask.
-## [Teste de pré-aula](https://white-water-09ec41f0f.azurestaticapps.net/quiz/17/)
+## [Teste de pré-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/17/)
## Criando um aplicativo
@@ -336,7 +336,7 @@ Em um ambiente profissional, você pode ver como uma boa comunicação é necess
Em vez de trabalhar em um notebook e importar o modelo para o aplicativo Flask, você poderia treinar o modelo dentro do aplicativo Flask! Tente converter seu código Python no notebook, talvez depois que seus dados forem limpos, para treinar o modelo de dentro do aplicativo em uma rota chamada `train`. Quais são os prós e contras de se buscar esse método?
-## [Teste pós-aula](https://white-water-09ec41f0f.azurestaticapps.net/quiz/18/)
+## [Teste pós-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/18/)
## Análise e autoestudo
diff --git a/3-Web-App/1-Web-App/translations/README.zh-cn.md b/3-Web-App/1-Web-App/translations/README.zh-cn.md
index c640980849f2b05636be15a4b5f7268001095da0..faf92e1a12da1f7c3685f3320f343d2e6822203c 100644
--- a/3-Web-App/1-Web-App/translations/README.zh-cn.md
+++ b/3-Web-App/1-Web-App/translations/README.zh-cn.md
@@ -11,7 +11,7 @@
为此，你需要使用 Flask 构建一个 web 应用程序。
-## [课前测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/17/)
+## [课前测](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/17/)
## 构建应用程序
@@ -334,7 +334,7 @@ print(model.predict([[50,44,-12]]))
你可以在 Flask 应用程序中训练模型，而不是在 notebook 上工作并将模型导入 Flask 应用程序！尝试在 notebook 中转换 Python 代码，可能是在清除数据之后，从应用程序中的一个名为 `train` 的路径训练模型。采用这种方法的利弊是什么？
-## [课后测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/18/)
+## [课后测](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/18/)
## 复习与自学
diff --git a/4-Classification/1-Introduction/README.md b/4-Classification/1-Introduction/README.md
index 03b0ba97494915a73622c18786b6eee9d9cb133a..1fd35e8cd59e33efb9245cfce306089ec859ba8c 100644
--- a/4-Classification/1-Introduction/README.md
+++ b/4-Classification/1-Introduction/README.md
@@ -19,7 +19,7 @@ Remember:
Classification uses various algorithms to determine other ways of determining a data point's label or class. Let's work with this cuisine data to see whether, by observing a group of ingredients, we can determine its cuisine of origin.
-## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/19/)
+## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/19/)
> ### [This lesson is available in R!](./solution/R/lesson_10-R.ipynb)
@@ -288,7 +288,7 @@ Now that you have cleaned the data, use [SMOTE](https://imbalanced-learn.org/dev
This curriculum contains several interesting datasets. Dig through the `data` folders and see if any contain datasets that would be appropriate for binary or multi-class classification? What questions would you ask of this dataset?
-## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/20/)
+## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/20/)
## Review & Self Study
diff --git a/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb b/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb
index a0e93536677bb999502ad0e42378cc6a2d15a215..9fb94e302503b980a192b7861fc8abe96f969fe0 100644
--- a/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb
+++ b/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb
@@ -50,7 +50,7 @@
"\n",
"Classification uses various algorithms to determine other ways of determining a data point's label or class. Let's work with this cuisine data to see whether, by observing a group of ingredients, we can determine its cuisine of origin.\n",
"\n",
- "### [**Pre-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/19/)\n",
+ "### [**Pre-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/19/)\n",
"\n",
"### **Introduction**\n",
"\n",
@@ -692,7 +692,7 @@
"\r\n",
"This curriculum contains several interesting datasets. Dig through the `data` folders and see if any contain datasets that would be appropriate for binary or multi-class classification? What questions would you ask of this dataset?\r\n",
"\r\n",
- "## [**Post-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/20/)\r\n",
+ "## [**Post-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/20/)\r\n",
"\r\n",
"## **Review & Self Study**\r\n",
"\r\n",
diff --git a/4-Classification/1-Introduction/solution/R/lesson_10.Rmd b/4-Classification/1-Introduction/solution/R/lesson_10.Rmd
index f31a7e0b8b942b7c0b1ab5fbe0c7d756675a5a5a..06959b65d650347e9af9ad0de19035bb37bbb827 100644
--- a/4-Classification/1-Introduction/solution/R/lesson_10.Rmd
+++ b/4-Classification/1-Introduction/solution/R/lesson_10.Rmd
@@ -26,7 +26,7 @@ Remember:
Classification uses various algorithms to determine other ways of determining a data point's label or class. Let's work with this cuisine data to see whether, by observing a group of ingredients, we can determine its cuisine of origin.
-### [**Pre-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/19/)
+### [**Pre-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/19/)
### **Introduction**
@@ -403,7 +403,7 @@ This fresh CSV can now be found in the root data folder.
This curriculum contains several interesting datasets. Dig through the `data` folders and see if any contain datasets that would be appropriate for binary or multi-class classification? What questions would you ask of this dataset?
-## [**Post-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/20/)
+## [**Post-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/20/)
## **Review & Self Study**
diff --git a/4-Classification/1-Introduction/translations/README.es.md b/4-Classification/1-Introduction/translations/README.es.md
index 60aa54bcb77053d09260209f7b65307cd97e4a95..b1ee47427c17b71deb94291150f25bf03282326a 100644
--- a/4-Classification/1-Introduction/translations/README.es.md
+++ b/4-Classification/1-Introduction/translations/README.es.md
@@ -19,7 +19,7 @@ Recuerda:
La clasificación utiliza varios algorítmos para determinar otras formas de determinar la clase o etiqueta de un punto de datos. Trabajemos con estos datos de cocina para ver si, al observar un grupo de ingredientes, podemos determinar su cocina u origen.
-## [Examen previo a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/19?loc=es)
+## [Examen previo a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/19?loc=es)
> ### [¡Esta lección está disponible en R!](./solution/R/lesson_10-R.ipynb)
@@ -288,7 +288,7 @@ Ahora que has limpiado los datos, usa [SMOTE](https://imbalanced-learn.org/dev/r
Este plan de estudios contiene varios conjuntos de datos interesantes. Profundiza en los directorios `data` y ve si alguno contiene conjuntos de datos que serían apropiados para clasificación binaria o multiclase. ¿Qué preguntas harías a este conunto de datos?
-## [Examen posterior a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/20?loc=es)
+## [Examen posterior a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/20?loc=es)
## Revisión y autoestudio
diff --git a/4-Classification/1-Introduction/translations/README.it.md b/4-Classification/1-Introduction/translations/README.it.md
index 8115bb8d1a5a66a4a8d848b73fac06526d70d274..5bfd7fb04473ee43d24b9b3f299bdf94b054e34c 100644
--- a/4-Classification/1-Introduction/translations/README.it.md
+++ b/4-Classification/1-Introduction/translations/README.it.md
@@ -19,7 +19,7 @@ Ricordare:
La classificazione utilizza vari algoritmi per determinare altri modi per definire l'etichetta o la classe di un punto dati. Si lavorerà con questi dati di cucina per vedere se, osservando un gruppo di ingredienti, è possibile determinarne la cucina di origine.
-## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/19/?loc=it)
+## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/19/?loc=it)
### Introduzione
@@ -286,7 +286,7 @@ Ora che i dati sono puliti, si usa [SMOTE](https://imbalanced-learn.org/dev/refe
Questo programma di studi contiene diversi insiemi di dati interessanti. Esaminare le cartelle `data` e vedere se contiene insiemi di dati che sarebbero appropriati per la classificazione binaria o multiclasse. Quali domande si farebbero a questo insieme di dati?
-## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/20/?loc=it)
+## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/20/?loc=it)
## Revisione e Auto Apprendimento
diff --git a/4-Classification/1-Introduction/translations/README.ko.md b/4-Classification/1-Introduction/translations/README.ko.md
index c1acee0701632f0f9469659d5de8d30ebc881ced..abe82d3d7e85ca032d8d1d09fe97801fb90062cb 100644
--- a/4-Classification/1-Introduction/translations/README.ko.md
+++ b/4-Classification/1-Introduction/translations/README.ko.md
@@ -19,7 +19,7 @@ Classification은 regression 기술과 공통점이 많은 [supervised learning]
Classification은 다양한 알고리즘으로 데이터 포인트의 라벨 혹은 클래스를 결정할 다른 방식을 고릅니다. 요리 데이터로, 재료 그룹을 찾아서, 전통 요리로 결정할 수 있는지 알아보려 합니다.
-## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/19/)
+## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/19/)
### 소개
@@ -287,7 +287,7 @@ Scikit-learn은 해결하고 싶은 문제의 타입에 따라서, 데이터를
해당 커리큘럼은 여러 흥미로운 데이터셋을 포함하고 있습니다. `data` 폴더를 파보면서 binary 또는 multi-class classification에 적당한 데이터셋이 포함되어 있나요? 데이터셋에 어떻게 물어보나요?
-## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/20/)
+## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/20/)
## 검토 & 자기주도 학습
diff --git a/4-Classification/1-Introduction/translations/README.pt-br.md b/4-Classification/1-Introduction/translations/README.pt-br.md
index c4b7b32f3dc3144613eccb85bd331f73b51cb4a7..68dd89cfc8995b3fcca2f43d1d08d4e9590d1f88 100644
--- a/4-Classification/1-Introduction/translations/README.pt-br.md
+++ b/4-Classification/1-Introduction/translations/README.pt-br.md
@@ -19,7 +19,7 @@ Lembre-se:
A classificação usa vários algoritmos para determinar outras maneiras de determinar o rótulo ou a classe de um ponto de dados ou objeto. Vamos trabalhar com dados sobre culinária para ver se, observando um grupo de ingredientes, podemos determinar sua culinária de origem.
-## [Questionário inicial](https://white-water-09ec41f0f.azurestaticapps.net/quiz/19/?loc=ptbr)
+## [Questionário inicial](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/19/?loc=ptbr)
> ### [Esta lição está disponível em R!](../solution/R/lesson_10-R.ipynb)
@@ -288,7 +288,7 @@ Agora que você limpou os dados, use a [SMOTE](https://imbalanced-learn.org/dev/
Esta lição contém vários _datasets_ interessantes. Explore os arquivos da pasta `data` e veja quais _datasets_ seriam apropriados para classificação binária ou multiclasse. Quais perguntas você faria sobre estes _datasets_?
-## [Questionário para fixação](https://white-water-09ec41f0f.azurestaticapps.net/quiz/20?loc=ptbr)
+## [Questionário para fixação](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/20?loc=ptbr)
## Revisão e Auto Aprendizagem
diff --git a/4-Classification/1-Introduction/translations/README.tr.md b/4-Classification/1-Introduction/translations/README.tr.md
index e1b32ec90be1c8f1df0a0fc1faab6173112546a1..876c1ce72a94dcfcaa03b742e87299ff3fad4924 100644
--- a/4-Classification/1-Introduction/translations/README.tr.md
+++ b/4-Classification/1-Introduction/translations/README.tr.md
@@ -19,7 +19,7 @@ Hatırlayın:
Sınıflandırma, bir veri noktasının etiketini veya sınıfını belirlemek için farklı yollar belirlemek üzere çeşitli algoritmalar kullanır. Bir grup malzemeyi gözlemleyerek kökeninin hangi mutfak olduğunu belirleyip belirleyemeyeceğimizi görmek için bu mutfak verisiyle çalışalım.
-## [Ders öncesi kısa sınavı](https://white-water-09ec41f0f.azurestaticapps.net/quiz/19/?loc=tr)
+## [Ders öncesi kısa sınavı](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/19/?loc=tr)
### Giriş
@@ -287,7 +287,7 @@ Veriyi temizlediniz, şimdi [SMOTE](https://imbalanced-learn.org/dev/references/
Bu öğretim programı farklı ilgi çekici veri setleri içermekte. `data` klasörlerini inceleyin ve ikili veya çok sınıflı sınıflandırma için uygun olabilecek veri setleri bulunduran var mı, bakın. Bu veri seti için hangi soruları sorabilirdiniz?
-## [Ders sonrası kısa sınavı](https://white-water-09ec41f0f.azurestaticapps.net/quiz/20/?loc=tr)
+## [Ders sonrası kısa sınavı](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/20/?loc=tr)
## Gözden Geçirme & Kendi Kendine Çalışma
diff --git a/4-Classification/1-Introduction/translations/README.zh-cn.md b/4-Classification/1-Introduction/translations/README.zh-cn.md
index b1d2862bbe55f6d6e38e43758ec8bfbbae8253a1..70954b6f35a2d5b15c74b1a12671a3a5fabcaca0 100644
--- a/4-Classification/1-Introduction/translations/README.zh-cn.md
+++ b/4-Classification/1-Introduction/translations/README.zh-cn.md
@@ -19,7 +19,7 @@
分类方法采用多种算法来确定其他可以用来确定一个数据点的标签或类别的方法。让我们来研究一下这个数据集，看看我们能否通过观察菜肴的原料来确定它的源头。
-## [课程前的小问题](https://white-water-09ec41f0f.azurestaticapps.net/quiz/19/)
+## [课程前的小问题](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/19/)
分类是机器学习研究者和数据科学家使用的一种基本方法。从基本的二元分类（这是不是一份垃圾邮件？）到复杂的图片分类和使用计算机视觉的分割技术，它都是将数据分类并提出相关问题的有效工具。
@@ -280,7 +280,7 @@ Scikit-learn 项目提供多种对数据进行分类的算法，你需要根据
本项目的全部课程含有很多有趣的数据集。 探索一下 `data` 文件夹，看看这里面有没有适合二元分类、多元分类算法的数据集，再想一下你对这些数据集有没有什么想问的问题。
-## [课后练习](https://white-water-09ec41f0f.azurestaticapps.net/quiz/20/)
+## [课后练习](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/20/)
## 回顾 & 自学
diff --git a/4-Classification/2-Classifiers-1/README.md b/4-Classification/2-Classifiers-1/README.md
index c1e953ce4b85a4f2af7a6036dffead92be837cf7..7555000c8986d00404f8ef9f66efe1c1aeff2579 100644
--- a/4-Classification/2-Classifiers-1/README.md
+++ b/4-Classification/2-Classifiers-1/README.md
@@ -4,7 +4,7 @@ In this lesson, you will use the dataset you saved from the last lesson full of
You will use this dataset with a variety of classifiers to _predict a given national cuisine based on a group of ingredients_. While doing so, you'll learn more about some of the ways that algorithms can be leveraged for classification tasks.
-## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/21/)
+## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/21/)
# Preparation
Assuming you completed [Lesson 1](../1-Introduction/README.md), make sure that a _cleaned_cuisines.csv_ file exists in the root `/data` folder for these four lessons.
@@ -231,7 +231,7 @@ Since you are using the multiclass case, you need to choose what _scheme_ to use
In this lesson, you used your cleaned data to build a machine learning model that can predict a national cuisine based on a series of ingredients. Take some time to read through the many options Scikit-learn provides to classify data. Dig deeper into the concept of 'solver' to understand what goes on behind the scenes.
-## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/22/)
+## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/22/)
## Review & Self Study
diff --git a/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb b/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb
index 2d88245aae50f5533eaa6d7d0897ec8fac91f77f..63d423914540cbfe8561b28a43f5332447f2ecd5 100644
--- a/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb
+++ b/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb
@@ -33,7 +33,7 @@
"\n",
"In this lesson, we'll explore a variety of classifiers to *predict a given national cuisine based on a group of ingredients.* While doing so, we'll learn more about some of the ways that algorithms can be leveraged for classification tasks.\n",
"\n",
- "### [**Pre-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/21/)\n",
+ "### [**Pre-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/21/)\n",
"\n",
"### **Preparation**\n",
"\n",
diff --git a/4-Classification/2-Classifiers-1/solution/R/lesson_11.Rmd b/4-Classification/2-Classifiers-1/solution/R/lesson_11.Rmd
index c59d5c90bc1134fa9f5299ce2a11c8999bd48740..000142ecee39c71b2c177d9ce374f153d9dc7756 100644
--- a/4-Classification/2-Classifiers-1/solution/R/lesson_11.Rmd
+++ b/4-Classification/2-Classifiers-1/solution/R/lesson_11.Rmd
@@ -14,7 +14,7 @@ output:
In this lesson, we'll explore a variety of classifiers to *predict a given national cuisine based on a group of ingredients.* While doing so, we'll learn more about some of the ways that algorithms can be leveraged for classification tasks.
-### [**Pre-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/21/)
+### [**Pre-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/21/)
### **Preparation**
diff --git a/4-Classification/2-Classifiers-1/translations/README.es.md b/4-Classification/2-Classifiers-1/translations/README.es.md
index 64792f7937a604d250ba8cca80900d020e4cd148..1568d172fa9f4b0c06a26c141603d86531a8c6ad 100644
--- a/4-Classification/2-Classifiers-1/translations/README.es.md
+++ b/4-Classification/2-Classifiers-1/translations/README.es.md
@@ -4,7 +4,7 @@ En esta lección, usarás el conjunto de datos que guardaste en la última lecci
Usarás este conjunto de datos con una variedad de clasificadores para _predecir una cocina nacional dada basado en un grupo de ingredientes_. Mientras lo haces, aprenderás más acerca de algunas formas en que los algoritmos pueden ser aprovechados para las tareas de clasificación.
-## [Examen previo a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/21?loc=es)
+## [Examen previo a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/21?loc=es)
# Preparación
@@ -234,7 +234,7 @@ Ya que estás usando un caso multiclase, necesitas elegir qué _esquema_ usar y
En esta lección, usaste tus datos limpios para construir un modelo de aprendizaje automático que puede predecir una cocina nacional basado en una serie de ingredientes. Toma un tiempo para leer las diversas opciones que provee Scikit-learn para clasificar los datos. Profundiza en el concepto de 'solucionador' para comprender que sucede detrás de escena.
-## [Examen posterior a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/22?loc=es)
+## [Examen posterior a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/22?loc=es)
## Revisión y autoestudio
diff --git a/4-Classification/2-Classifiers-1/translations/README.it.md b/4-Classification/2-Classifiers-1/translations/README.it.md
index 4128c51034ae642e30e6486e557823ead801d403..ed85e784a853426316d9c5d42724d173f3cd1ec9 100644
--- a/4-Classification/2-Classifiers-1/translations/README.it.md
+++ b/4-Classification/2-Classifiers-1/translations/README.it.md
@@ -4,7 +4,7 @@ In questa lezione, si utilizzerà l'insieme di dati salvati dall'ultima lezione,
Si utilizzerà questo insieme di dati con una varietà di classificatori per _prevedere una determinata cucina nazionale in base a un gruppo di ingredienti_. Mentre si fa questo, si imparerà di più su alcuni dei modi in cui gli algoritmi possono essere sfruttati per le attività di classificazione.
-## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/21/?loc=it)
+## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/21/?loc=it)
# Preparazione
Supponendo che la [Lezione 1](../1-Introduction/README.md) sia stata completata, assicurarsi che _esista_ un file clean_cuisines.csv nella cartella in radice `/data` per queste quattro lezioni.
@@ -232,7 +232,7 @@ Poiché si sta utilizzando il caso multiclasse, si deve scegliere quale _schema_
In questa lezione, sono stati utilizzati dati puliti per creare un modello di apprendimento automatico in grado di prevedere una cucina nazionale basata su una serie di ingredienti. Si prenda del tempo per leggere le numerose opzioni fornite da Scikit-learn per classificare i dati. Approfondire il concetto di "risolutore" per capire cosa succede dietro le quinte.
-## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/22/?loc=it)
+## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/22/?loc=it)
## Revisione e Auto Apprendimento
Approfondire un po' la matematica alla base della regressione logistica in [questa lezione](https://people.eecs.berkeley.edu/~russell/classes/cs194/f11/lectures/CS194%20Fall%202011%20Lecture%2006.pdf)
diff --git a/4-Classification/2-Classifiers-1/translations/README.ko.md b/4-Classification/2-Classifiers-1/translations/README.ko.md
index e2c5c95979c6e555281755537e7f0096afa3176d..d6db0d0777d71e28d5bf9acf064fd7fde32882ec 100644
--- a/4-Classification/2-Classifiers-1/translations/README.ko.md
+++ b/4-Classification/2-Classifiers-1/translations/README.ko.md
@@ -4,7 +4,7 @@
다양한 classifiers와 데이터셋을 사용해서 _재료 그룹 기반으로 주어진 국민 요리를 예측_ 합니다. 이러는 동안, classification 작업에 알고리즘을 활용할 몇 방식에 대해 자세히 배워볼 예정입니다.
-## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/21/)
+## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/21/)
## 준비하기
@@ -233,7 +233,7 @@ multiclass 케이스로, 사용할 _scheme_ 와 설정할 _solver_ 를 선택해
이 강의에서, 정리된 데이터로 재료의 시리즈를 기반으로 국민 요리를 예측할 수 있는 머신러닝 모델을 만들었습니다. 시간을 투자해서 Scikit-learn이 데이터를 분류하기 위해 제공하는 다양한 옵션을 읽어봅니다. 무대 뒤에서 생기는 일을 이해하기 위해서 'solver'의 개념을 깊게 파봅니다.
-## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/22/)
+## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/22/)
## 검토 & 자기주도 학습
[this lesson](https://people.eecs.berkeley.edu/~russell/classes/cs194/f11/lectures/CS194%20Fall%202011%20Lecture%2006.pdf)에서 logistic regression 뒤의 수학에 대해서 더 자세히 파봅니다.
diff --git a/4-Classification/2-Classifiers-1/translations/README.pt-br.md b/4-Classification/2-Classifiers-1/translations/README.pt-br.md
index 837bd293c45b084699382da49865a7a0782bcf46..193f8d857d1e519e532b9fc8036be1ca72c07202 100644
--- a/4-Classification/2-Classifiers-1/translations/README.pt-br.md
+++ b/4-Classification/2-Classifiers-1/translations/README.pt-br.md
@@ -4,7 +4,7 @@ Nesta lição, você usará o _dataset_ balanceado e tratado que salvou da últi
Você usará este _dataset_ com uma variedade de classificadores para _prever uma determinada culinária nacional com base em um grupo de ingredientes_. Enquanto isso, você aprenderá mais sobre algumas das maneiras como os algoritmos podem ser aproveitados para tarefas de classificação.
-## [Questionário inicial](https://white-water-09ec41f0f.azurestaticapps.net/quiz/21?loc=ptbr)
+## [Questionário inicial](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/21?loc=ptbr)
# Preparação
@@ -232,7 +232,7 @@ Já que estamos usando um caso multiclasse, você precisa escolher qual _scheme_
Nesta lição, você usou seus dados para construir um modelo de aprendizado de máquina que pode prever uma culinária nacional com base em uma série de ingredientes. Reserve algum tempo para ler as opções que o Scikit-learn oferece para classificar dados. Aprofunde-se no conceito de 'solucionador' para entender o que acontece nos bastidores.
-## [Questionário para fixação](https://white-water-09ec41f0f.azurestaticapps.net/quiz/22?loc=ptbr)
+## [Questionário para fixação](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/22?loc=ptbr)
## Revisão e Auto Aprendizagem
diff --git a/4-Classification/2-Classifiers-1/translations/README.tr.md b/4-Classification/2-Classifiers-1/translations/README.tr.md
index 30f36b133cc1ab69c522d505318a6c3cb5caf0c5..f59a39a838befb5d9972e59a08dccd8c3e90584e 100644
--- a/4-Classification/2-Classifiers-1/translations/README.tr.md
+++ b/4-Classification/2-Classifiers-1/translations/README.tr.md
@@ -4,7 +4,7 @@ Bu derste, mutfaklarla ilgili dengeli ve temiz veriyle dolu, geçen dersten kayd
Bu veri setini çeşitli sınıflandırıcılarla _bir grup malzemeyi baz alarak verilen bir ulusal mutfağı öngörmek_ için kullanacaksınız. Bunu yaparken, sınıflandırma görevleri için algoritmaların leveraj edilebileceği yollardan bazıları hakkında daha fazla bilgi edineceksiniz.
-## [Ders öncesi kısa sınavı](https://white-water-09ec41f0f.azurestaticapps.net/quiz/21/?loc=tr)
+## [Ders öncesi kısa sınavı](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/21/?loc=tr)
# Hazırlık
[Birinci dersi](../../1-Introduction/README.md) tamamladığınızı varsayıyoruz, dolayısıyla bu dört ders için _cleaned_cuisines.csv_ dosyasının kök `/data` klasöründe var olduğundan emin olun.
@@ -231,7 +231,7 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine
Bu derste, bir grup malzemeyi baz alarak bir ulusal mutfağı öngörebilen bir makine öğrenimi modeli oluşturmak için temiz verinizi kullandınız. Scikit-learn'ün veri sınıflandırmak için sağladığı birçok yöntemi okumak için biraz vakit ayırın. Arka tarafta neler olduğunu anlamak için 'çözücü' kavramını derinlemesine inceleyin.
-## [Ders sonrası kısa sınavı](https://white-water-09ec41f0f.azurestaticapps.net/quiz/22/?loc=tr)
+## [Ders sonrası kısa sınavı](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/22/?loc=tr)
## Gözden geçirme & kendi kendine çalışma
diff --git a/4-Classification/2-Classifiers-1/translations/README.zh-cn.md b/4-Classification/2-Classifiers-1/translations/README.zh-cn.md
index 0c08c17f9f961808bae6284199178fa7c83d38a7..06761a1066633cbb44144bc7e61542528e0c5826 100644
--- a/4-Classification/2-Classifiers-1/translations/README.zh-cn.md
+++ b/4-Classification/2-Classifiers-1/translations/README.zh-cn.md
@@ -4,7 +4,7 @@
你将使用此数据集和各种分类器，_根据一组配料预测这是哪一国家的美食_。在此过程中，你将学到更多用来权衡分类任务算法的方法
-## [课前测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/21/)
+## [课前测验](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/21/)
# 准备工作
@@ -230,7 +230,7 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine
在本课程中，你使用了清洗后的数据建立了一个机器学习的模型，这个模型能够根据输入的一系列的配料来预测菜品来自于哪个国家。请再花点时间阅读一下 Scikit-learn 所提供的关于可以用来分类数据的其他方法的资料。此外，你也可以深入研究一下“solver”的概念并尝试一下理解其背后的原理。
-## [课后测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/22/)
+## [课后测验](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/22/)
## 回顾与自学
diff --git a/4-Classification/3-Classifiers-2/README.md b/4-Classification/3-Classifiers-2/README.md
index 00675169695dca47acfd63ab3871401a3b1df345..014a4662aaa42d17ba4de0aa052f86773e65d6ad 100644
--- a/4-Classification/3-Classifiers-2/README.md
+++ b/4-Classification/3-Classifiers-2/README.md
@@ -2,7 +2,7 @@
In this second classification lesson, you will explore more ways to classify numeric data. You will also learn about the ramifications for choosing one classifier over the other.
-## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/23/)
+## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/23/)
### Prerequisite
@@ -224,7 +224,7 @@ This method of Machine Learning "combines the predictions of several base estima
Each of these techniques has a large number of parameters that you can tweak. Research each one's default parameters and think about what tweaking these parameters would mean for the model's quality.
-## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/24/)
+## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/24/)
## Review & Self Study
diff --git a/4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb b/4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb
index d1a6fbf2b81d335a3d515e6d0dd5348e87f30d71..4c22b93eb360f390bffbda133ac2f75393706fed 100644
--- a/4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb
+++ b/4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb
@@ -35,7 +35,7 @@
"\n",
"In this second classification lesson, we will explore `more ways` to classify categorical data. We will also learn about the ramifications for choosing one classifier over the other.\n",
"\n",
- "### [**Pre-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/23/)\n",
+ "### [**Pre-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/23/)\n",
"\n",
"### **Prerequisite**\n",
"\n",
@@ -619,7 +619,7 @@
"\n",
"> In practice, we usually *estimate* the *best values* for these by training many models on a `simulated data set` and measuring how well all these models perform. This process is called **tuning**.\n",
"\n",
- "### [**Post-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/24/)\n",
+ "### [**Post-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/24/)\n",
"\n",
"### **Review & Self Study**\n",
"\n",
diff --git a/4-Classification/3-Classifiers-2/solution/R/lesson_12.Rmd b/4-Classification/3-Classifiers-2/solution/R/lesson_12.Rmd
index 3a6f6ba43114260f875e23acc5b458abc1a10dc6..526b8503f8d552595b09adc037f2ed32839cef35 100644
--- a/4-Classification/3-Classifiers-2/solution/R/lesson_12.Rmd
+++ b/4-Classification/3-Classifiers-2/solution/R/lesson_12.Rmd
@@ -14,7 +14,7 @@ output:
In this second classification lesson, we will explore `more ways` to classify categorical data. We will also learn about the ramifications for choosing one classifier over the other.
-### [**Pre-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/23/)
+### [**Pre-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/23/)
### **Prerequisite**
@@ -433,7 +433,7 @@ To find out more about a particular model and its parameters, use: `help("model"
> In practice, we usually *estimate* the *best values* for these by training many models on a `simulated data set` and measuring how well all these models perform. This process is called **tuning**.
-### [**Post-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/24/)
+### [**Post-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/24/)
### **Review & Self Study**
diff --git a/4-Classification/3-Classifiers-2/translations/README.es.md b/4-Classification/3-Classifiers-2/translations/README.es.md
index 1e81e46c214040f7df34c36723c9e6ed67bd29cb..bd16d23ff1c2b97257cce1b27fd2d608a55124cb 100644
--- a/4-Classification/3-Classifiers-2/translations/README.es.md
+++ b/4-Classification/3-Classifiers-2/translations/README.es.md
@@ -2,7 +2,7 @@
En esta segunda lección de clasificación, explorarás más formas de clasificar datos numéricos. También aprenderás acerca de las ramificaciones para elegir un clasificador en lugar de otro.
-## [Examen previo a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/23?loc=es)
+## [Examen previo a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/23?loc=es)
### Prerrequisito
@@ -223,7 +223,7 @@ Este método de aprendizaje automático "combina las predicciones de varios esti
Cada una de estas técnicas tiene un gran número de parámetros que puedes modificar. Investiga los parámetros predeterminados de cada uno y piensa en lo que significaría el ajuste de estos parámetros para la calidad del modelo.
-## [Examen posterior a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/24?loc=es)
+## [Examen posterior a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/24?loc=es)
## Revisión y autoestudio
diff --git a/4-Classification/3-Classifiers-2/translations/README.it.md b/4-Classification/3-Classifiers-2/translations/README.it.md
index 5294a7063e97242ec8eb464e86966b2d47798209..8f4fdd03e2126ea4ec777bfd6384609c88606c30 100644
--- a/4-Classification/3-Classifiers-2/translations/README.it.md
+++ b/4-Classification/3-Classifiers-2/translations/README.it.md
@@ -2,7 +2,7 @@
In questa seconda lezione sulla classificazione, si esploreranno più modi per classificare i dati numerici. Si Impareranno anche le ramificazioni per la scelta di un classificatore rispetto all'altro.
-## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/23/?loc=it)
+## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/23/?loc=it)
### Prerequisito
@@ -224,7 +224,7 @@ Questo metodo di Machine Learning "combina le previsioni di diversi stimatori di
Ognuna di queste tecniche ha un gran numero di parametri che si possono modificare. Ricercare i parametri predefiniti di ciascuno e pensare a cosa significherebbe modificare questi parametri per la qualità del modello.
-## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/24/?loc=it)
+## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/24/?loc=it)
## Revisione e Auto Apprendimento
diff --git a/4-Classification/3-Classifiers-2/translations/README.ko.md b/4-Classification/3-Classifiers-2/translations/README.ko.md
index 9438c4308b2c764f4646a77fa1ed5afe62b2e53e..78b672687a34acff882805214cb7f624e703abbd 100644
--- a/4-Classification/3-Classifiers-2/translations/README.ko.md
+++ b/4-Classification/3-Classifiers-2/translations/README.ko.md
@@ -2,7 +2,7 @@
두번째 classification 강의에서, 숫자 데이터를 분류하는 더 많은 방식을 알아봅니다. 다른 것보다 하나의 classifier를 선택하는 파급효과도 배우게 됩니다.
-## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/23/)
+## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/23/)
### 필요 조건
@@ -224,7 +224,7 @@ weighted avg 0.73 0.72 0.72 1199
각 기술에는 트윅할 수 있는 많은 수의 파라미터가 존재합니다. 각 기본 파라미터를 조사하고 파라미터를 조절헤서 모델 품질에 어떤 의미가 부여되는지 생각합니다.
-## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/24/)
+## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/24/)
## 검토 & 자기주도 학습
diff --git a/4-Classification/3-Classifiers-2/translations/README.pt-br.md b/4-Classification/3-Classifiers-2/translations/README.pt-br.md
index ec01ef5cc477456c80c56cd6ad5f2cd2bcdebe2c..4315d9e3843b9979f691854c8baf0b78156521e3 100644
--- a/4-Classification/3-Classifiers-2/translations/README.pt-br.md
+++ b/4-Classification/3-Classifiers-2/translations/README.pt-br.md
@@ -2,7 +2,7 @@
Nesta segunda lição de classificação, você explorará outras maneiras de classificar dados numéricos. Você também aprenderá sobre as ramificações para escolher um classificador em vez de outro.
-## [Questionário inicial](https://white-water-09ec41f0f.azurestaticapps.net/quiz/23?loc=ptbr)
+## [Questionário inicial](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/23?loc=ptbr)
### Pré-requisito
@@ -224,7 +224,7 @@ Este método de arendizado de máquina "combina as previsões de vários estimad
Cada uma dessas técnicas possui um grande número de parâmetros. Pesquise os parâmetros padrão de cada um e pense no que o ajuste desses parâmetros significaria para a qualidade do modelo.
-## [Questionário para fixação](https://white-water-09ec41f0f.azurestaticapps.net/quiz/24?loc=ptbr)
+## [Questionário para fixação](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/24?loc=ptbr)
## Revisão e Auto Aprendizagem
diff --git a/4-Classification/3-Classifiers-2/translations/README.tr.md b/4-Classification/3-Classifiers-2/translations/README.tr.md
index 56c1e11e9fd12afb060f092867c16d7872ae30dd..aba0b99b96a542c3bbbb96409d3eb2e4dac034e3 100644
--- a/4-Classification/3-Classifiers-2/translations/README.tr.md
+++ b/4-Classification/3-Classifiers-2/translations/README.tr.md
@@ -2,7 +2,7 @@
Bu ikinci sınıflandırma dersinde, sayısal veriyi sınıflandırmak için daha fazla yöntem öğreneceksiniz. Ayrıca, bir sınıflandırıcıyı diğerlerine tercih etmenin sonuçlarını da öğreneceksiniz.
-## [Ders öncesi kısa sınavı](https://white-water-09ec41f0f.azurestaticapps.net/quiz/23/?loc=tr)
+## [Ders öncesi kısa sınavı](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/23/?loc=tr)
### Ön koşul
@@ -224,7 +224,7 @@ Makine Öğreniminin bu yöntemi, modelin kalitesini artırmak için, "birçok t
Bu yöntemlerden her biri değiştirebileceğiniz birsürü parametre içeriyor. Her birinin varsayılan parametrelerini araştırın ve bu parametreleri değiştirmenin modelin kalitesi için ne anlama gelebileceği hakkında düşünün.
-## [Ders sonrası kısa sınavı](https://white-water-09ec41f0f.azurestaticapps.net/quiz/24/?loc=tr)
+## [Ders sonrası kısa sınavı](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/24/?loc=tr)
## Gözden Geçirme & Kendi Kendine Çalışma
diff --git a/4-Classification/3-Classifiers-2/translations/README.zh-cn.md b/4-Classification/3-Classifiers-2/translations/README.zh-cn.md
index f26f2a4214a30fa5c27e337cf1ade438c49ef18c..203daf04614cd6c55ff13ccfa0a1a9974b1c49c0 100644
--- a/4-Classification/3-Classifiers-2/translations/README.zh-cn.md
+++ b/4-Classification/3-Classifiers-2/translations/README.zh-cn.md
@@ -2,7 +2,7 @@
在第二节课程中，您将探索更多方法来对数值数据进行分类。您还将了解选择不同的分类器所带来的结果。
-## [课前测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/23/)
+## [课前测验](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/23/)
### 先决条件
@@ -224,7 +224,7 @@ weighted avg 0.73 0.72 0.72 1199
这些技术方法每个都有很多能够让您微调的参数。研究每一个的默认参数，并思考调整这些参数对模型质量有何意义。
-## [课后测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/24/)
+## [课后测验](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/24/)
## 回顾与自学
diff --git a/4-Classification/4-Applied/README.md b/4-Classification/4-Applied/README.md
index ef63b2b6f4fbf543affb6dcf8ee84cdaf0c58746..baf63da13f2239e5ef5f68814265c0ab17758180 100644
--- a/4-Classification/4-Applied/README.md
+++ b/4-Classification/4-Applied/README.md
@@ -8,7 +8,7 @@ One of the most useful practical uses of machine learning is building recommenda
> 🎥 Click the image above for a video: Jen Looper builds a web app using classified cuisine data
-## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/25/)
+## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/25/)
In this lesson you will learn:
@@ -299,7 +299,7 @@ Congratulations, you have created a 'recommendation' web app with a few fields.
Your web app is very minimal, so continue to build it out using ingredients and their indexes from the [ingredient_indexes](../data/ingredient_indexes.csv) data. What flavor combinations work to create a given national dish?
-## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/26/)
+## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/26/)
## Review & Self Study
diff --git a/4-Classification/4-Applied/translations/README.es.md b/4-Classification/4-Applied/translations/README.es.md
index 609622103d408ad56ea1a6a3bababae6c78417c6..161db84c41043646f312cf09bb19e6fc02bf35d5 100644
--- a/4-Classification/4-Applied/translations/README.es.md
+++ b/4-Classification/4-Applied/translations/README.es.md
@@ -8,7 +8,7 @@ Uno de los usos prácticos más útiles del aprendizaje automático es construir
> 🎥 Haz clic en la imagen de arriba para ver el video: Jen Looper construye una aplicación web usando los datos clasificados de cocina.
-## [Examen previo a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/25?loc=es)
+## [Examen previo a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/25?loc=es)
En esta lección aprenderás:
@@ -301,7 +301,7 @@ Felicidades, has creado una aplicación de 'recomendación' con pocos campos. ¡
Tu aplicación web es mínima, así que continua construyéndola usando los ingredientes y sus índices de los datos [ingredient_indexes](../../data/ingredient_indexes.csv). ¿Qué combinaciones de sabor funcionan para crear un determinado platillo nacional?
-## [Examen posterior a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/26?loc=es)
+## [Examen posterior a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/26?loc=es)
## Revisión y autoestudio
diff --git a/4-Classification/4-Applied/translations/README.it.md b/4-Classification/4-Applied/translations/README.it.md
index b106ba009596998a0bb30945f0fff7ceeb6c8962..4e4816dc5b8d2ba6dfeaea11530fe46b4b4dab69 100644
--- a/4-Classification/4-Applied/translations/README.it.md
+++ b/4-Classification/4-Applied/translations/README.it.md
@@ -8,7 +8,7 @@ Uno degli usi pratici più utili dell'apprendimento automatico è la creazione d
> 🎥 Fare clic sull'immagine sopra per un video
-## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/25/?loc=it)
+## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/25/?loc=it)
In questa lezione, si imparerà:
@@ -321,7 +321,7 @@ Congratulazioni, si è creato un'app web di "raccomandazione" con pochi campi. S
L'app web è molto minimale, quindi continuare a costruirla usando gli ingredienti e i loro indici dai dati [ingredient_indexes](../../data/ingredient_indexes.csv) . Quali combinazioni di sapori funzionano per creare un determinato piatto nazionale?
-## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/26/?loc=it)
+## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/26/?loc=it)
## Revisione e Auto Apprendimento
diff --git a/4-Classification/4-Applied/translations/README.ko.md b/4-Classification/4-Applied/translations/README.ko.md
index d4493a9e54b689775ccbdaf9f37220b34cac3346..3d9e2794fa3457d3918f9ad491ea91446ba0d81a 100644
--- a/4-Classification/4-Applied/translations/README.ko.md
+++ b/4-Classification/4-Applied/translations/README.ko.md
@@ -8,7 +8,7 @@
> 🎥 영상 보려면 이미지 클릭
-## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/25/)
+## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/25/)
이 강의에서 다음을 배우게 됩니다:
@@ -322,7 +322,7 @@ index.html 파일의 폴더에서 Visual Studio Code로 터미널 세션을 엽
이 웹 앱은 매우 작아서, [ingredient_indexes](../../data/ingredient_indexes.csv) 데이터에서 성분과 인덱스로 계속 만듭니다. 주어진 국민 요리를 만드려면 어떤 풍미 조합으로 작업해야 되나요?
-## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/26/)
+## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/26/)
## 검토 & 자기주도 학습
diff --git a/4-Classification/4-Applied/translations/README.pt-br.md b/4-Classification/4-Applied/translations/README.pt-br.md
index f66463dc51147b740ee64c75fe41a7480bc95cf3..f7fc72141fe646e522910282570a316cfbcc15fa 100644
--- a/4-Classification/4-Applied/translations/README.pt-br.md
+++ b/4-Classification/4-Applied/translations/README.pt-br.md
@@ -8,7 +8,7 @@ Um dos usos práticos mais úteis do aprendizado de máquina é criar sistemas d
> 🎥 Clique na imagem acima para ver um vídeo
-## [Questionário inicial](https://white-water-09ec41f0f.azurestaticapps.net/quiz/25?loc=ptbr)
+## [Questionário inicial](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/25?loc=ptbr)
Nesta lição você aprenderá:
@@ -322,7 +322,7 @@ Parabéns, você criou uma aplicação Web de 'recomendação' com alguns campos
Sua aplicação é simples, portanto, adicione outros ingredientes observando seus índices na [planilha de ingredientes](../../data/ingredient_indexes.csv). Que combinações de sabores funcionam para criar um determinado prato?
-## [Questionário para fixação](https://white-water-09ec41f0f.azurestaticapps.net/quiz/26?loc=ptbr)
+## [Questionário para fixação](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/26?loc=ptbr)
## Revisão e Auto Aprendizagem
diff --git a/4-Classification/4-Applied/translations/README.tr.md b/4-Classification/4-Applied/translations/README.tr.md
index f46f31645c441b5d7b084540d664650f616fdd3a..d30610a14b6376819d7c0b4332035d0a27501ed0 100644
--- a/4-Classification/4-Applied/translations/README.tr.md
+++ b/4-Classification/4-Applied/translations/README.tr.md
@@ -8,7 +8,7 @@ Makine öğreniminin en faydalı pratik kullanımlarından biri, önerici/tavsiy
> :movie_camera: Video için yukarıdaki fotoğrafa tıklayın
-## [Ders öncesi kısa sınavı](https://white-water-09ec41f0f.azurestaticapps.net/quiz/25/?loc=tr)
+## [Ders öncesi kısa sınavı](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/25/?loc=tr)
Bu derste şunları öğreneceksiniz:
@@ -321,7 +321,7 @@ Tebrikler, birkaç değişkenle bir 'önerici' web uygulaması oluşturdunuz! Bu
Web uygulamanız çok minimal, bu yüzden [ingredient_indexes](../../data/ingredient_indexes.csv) verisinden malzemeleri ve indexlerini kullanarak web uygulamanızı oluşturmaya devam edin. Verilen bir ulusal yemeği yapmak için hangi tat birleşimleri işe yarıyor?
-## [Ders sonrası kısa sınavı](https://white-water-09ec41f0f.azurestaticapps.net/quiz/26/?loc=tr)
+## [Ders sonrası kısa sınavı](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/26/?loc=tr)
## Gözden Geçirme & Kendi Kendine Çalışma
diff --git a/4-Classification/4-Applied/translations/README.zh-CN.md b/4-Classification/4-Applied/translations/README.zh-CN.md
index bc9e42573aef77fb2986f329743e32241ea64313..eed2a5ca75c7714a2def4aebc76a66ba958afc66 100644
--- a/4-Classification/4-Applied/translations/README.zh-CN.md
+++ b/4-Classification/4-Applied/translations/README.zh-CN.md
@@ -7,7 +7,7 @@
> 🎥 点击上面的图片查看视频
-## [课前测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/25/)
+## [课前测验](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/25/)
本节课程中您将会学习：
@@ -318,7 +318,7 @@ Netron 是查看您模型的有用工具。
您的 Web 应用程序还很小巧，所以继续使用[配料索引](../../data/ingredient_indexes.csv)中的配料数据和索引数据来构建它吧。用什么样的口味组合才能创造出一道特定的民族菜肴？
-## [课后测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/26/)
+## [课后测验](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/26/)
## 回顾与自学
diff --git a/5-Clustering/1-Visualize/README.md b/5-Clustering/1-Visualize/README.md
index 37068503e6e6dacec4e68b21cc6e9b10c856d89c..32f07019e4e99efcdbe0518eda9cefc8bd9e1207 100644
--- a/5-Clustering/1-Visualize/README.md
+++ b/5-Clustering/1-Visualize/README.md
@@ -5,7 +5,7 @@ Clustering is a type of [Unsupervised Learning](https://wikipedia.org/wiki/Unsup
[](https://youtu.be/ty2advRiWJM "No One Like You by PSquare")
> 🎥 Click the image above for a video. While you're studying machine learning with clustering, enjoy some Nigerian Dance Hall tracks - this is a highly rated song from 2014 by PSquare.
-## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/27/)
+## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/27/)
### Introduction
[Clustering](https://link.springer.com/referenceworkentry/10.1007%2F978-0-387-30164-8_124) is very useful for data exploration. Let's see if it can help discover trends and patterns in the way Nigerian audiences consume music.
@@ -317,7 +317,7 @@ In general, for clustering, you can use scatterplots to show clusters of data, s
In preparation for the next lesson, make a chart about the various clustering algorithms you might discover and use in a production environment. What kinds of problems is the clustering trying to address?
-## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/28/)
+## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/28/)
## Review & Self Study
diff --git a/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb b/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb
index 66f4403b72952646ce2ebc2444245f58eef1f763..a1862ba1b18f91361ff07d4dfdea810b308780b4 100644
--- a/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb
+++ b/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb
@@ -7,7 +7,7 @@
"\r\n",
"Clustering is a type of [Unsupervised Learning](https://wikipedia.org/wiki/Unsupervised_learning) that presumes that a dataset is unlabelled or that its inputs are not matched with predefined outputs. It uses various algorithms to sort through unlabeled data and provide groupings according to patterns it discerns in the data.\r\n",
"\r\n",
- "[**Pre-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/27/)\r\n",
+ "[**Pre-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/27/)\r\n",
"\r\n",
"### **Introduction**\r\n",
"\r\n",
@@ -439,7 +439,7 @@
"\n",
"In preparation for the next lesson, make a chart about the various clustering algorithms you might discover and use in a production environment. What kinds of problems is the clustering trying to address?\n",
"\n",
- "## [**Post-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/28/)\n",
+ "## [**Post-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/28/)\n",
"\n",
"## **Review & Self Study**\n",
"\n",
diff --git a/5-Clustering/1-Visualize/solution/R/lesson_14.Rmd b/5-Clustering/1-Visualize/solution/R/lesson_14.Rmd
index eeb30698107bcef7146404fa6c3b593521ab51b2..53c0cd214d501906c3b231b36731e77de6f251ed 100644
--- a/5-Clustering/1-Visualize/solution/R/lesson_14.Rmd
+++ b/5-Clustering/1-Visualize/solution/R/lesson_14.Rmd
@@ -14,7 +14,7 @@ output:
Clustering is a type of [Unsupervised Learning](https://wikipedia.org/wiki/Unsupervised_learning) that presumes that a dataset is unlabelled or that its inputs are not matched with predefined outputs. It uses various algorithms to sort through unlabeled data and provide groupings according to patterns it discerns in the data.
-[**Pre-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/27/)
+[**Pre-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/27/)
### **Introduction**
@@ -315,7 +315,7 @@ In general, for clustering, you can use scatterplots to show clusters of data, s
In preparation for the next lesson, make a chart about the various clustering algorithms you might discover and use in a production environment. What kinds of problems is the clustering trying to address?
-## [**Post-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/28/)
+## [**Post-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/28/)
## **Review & Self Study**
diff --git a/5-Clustering/1-Visualize/translations/README.es.md b/5-Clustering/1-Visualize/translations/README.es.md
index 6a47ea3003f98652026a4db10b946856962511d1..852aa44541f64690a6df76d4157de9ba1fd579ec 100644
--- a/5-Clustering/1-Visualize/translations/README.es.md
+++ b/5-Clustering/1-Visualize/translations/README.es.md
@@ -6,7 +6,7 @@ El agrupamiento (clustering) es un tipo de [aprendizaje no supervisado](https://
> 🎥 Haz clic en la imagen de arriba para ver el video. Mientras estudias aprendizaje automático con agrupamiento, disfruta de algunas canciones Dance Hall Nigerianas - esta es una canción muy popular del 2014 de PSquare.
-## [Examen previo a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/27?loc=es)
+## [Examen previo a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/27?loc=es)
### Introducción
@@ -320,7 +320,7 @@ En general, para el agrupamiento, puedes usar gráficos de dispersión para most
En preparación para la siguiente lección, realiza una gráfica acerca de los diverso algoritmos de agrupamiento que puedes descubrir y usar en un ambiente de producción. ¿Qué tipo de problemas trata de abordar el agrupamiento?
-## [Examen porterior a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/28?loc=es)
+## [Examen porterior a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/28?loc=es)
## Revisión y auto-estudio
diff --git a/5-Clustering/1-Visualize/translations/README.it.md b/5-Clustering/1-Visualize/translations/README.it.md
index 3da903d4f8d809e364f0fd46858fcfa074b519bc..1c9d832356d0fb4263eb9b3950fc693f7578d884 100644
--- a/5-Clustering/1-Visualize/translations/README.it.md
+++ b/5-Clustering/1-Visualize/translations/README.it.md
@@ -5,7 +5,7 @@ Il clustering è un tipo di [apprendimento non supervisionato](https://wikipedia
[](https://youtu.be/ty2advRiWJM "No One Like You di PSquare")
> 🎥 Fare clic sull'immagine sopra per un video. Mentre si studia machine learning con il clustering, si potranno gradire brani della Nigerian Dance Hall: questa è una canzone molto apprezzata del 2014 di PSquare.
-## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/27/?loc=it)
+## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/27/?loc=it)
### Introduzione
@@ -319,7 +319,7 @@ In generale, per il clustering è possibile utilizzare i grafici a dispersione p
In preparazione per la lezione successiva, creare un grafico sui vari algoritmi di clustering che si potrebbero scoprire e utilizzare in un ambiente di produzione. Che tipo di problemi sta cercando di affrontare il clustering?
-## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/28/?loc=it)
+## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/28/?loc=it)
## Revisione e Auto Apprendimento
diff --git a/5-Clustering/1-Visualize/translations/README.ko.md b/5-Clustering/1-Visualize/translations/README.ko.md
index c561bd621f6d6a716feb26f9ebc6ff396814d5ed..8f82c2a0ea78772419f0cb0916f01b998dc4a793 100644
--- a/5-Clustering/1-Visualize/translations/README.ko.md
+++ b/5-Clustering/1-Visualize/translations/README.ko.md
@@ -6,7 +6,7 @@ Clustering이 데이터셋에 라벨을 붙이지 않거나 입력이 미리 정
> 🎥 영상을 보려면 이미지 클릭. While you're studying machine learning with clustering, enjoy some Nigerian Dance Hall tracks - this is a highly rated song from 2014 by PSquare.
-## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/27/)
+## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/27/)
### 소개
@@ -322,7 +322,7 @@ Clustering이 데이터셋에 라벨을 붙이지 않거나 입력이 미리 정
다음 강의를 준비하기 위해서, 프로덕션 환경에서 찾아서 사용할 수 있는 다양한 clustering 알고리즘을 차트로 만듭니다. clustering은 어떤 문제를 해결하려고 시도하나요?
-## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/28/)
+## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/28/)
## 검토 & 자기주도 학습
diff --git a/5-Clustering/1-Visualize/translations/README.zh-cn.md b/5-Clustering/1-Visualize/translations/README.zh-cn.md
index c3aae1dcb2a2c87d70cb517845d2f227e514a2ec..e5a557ebe3c46d96ccbb727deee73cf92311c9a0 100644
--- a/5-Clustering/1-Visualize/translations/README.zh-cn.md
+++ b/5-Clustering/1-Visualize/translations/README.zh-cn.md
@@ -6,7 +6,7 @@
> 🎥 点击上面的图片观看视频。当您通过聚类学习机器学习时，请欣赏一些尼日利亚舞厅曲目 - 这是 2014 年 PSquare 上高度评价的歌曲。
-## [课前测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/27/)
+## [课前测验](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/27/)
### 介绍
@@ -326,7 +326,7 @@
聚类试图解决什么样的问题？
-## [课后测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/28/)
+## [课后测验](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/28/)
## 复习与自学
diff --git a/5-Clustering/2-K-Means/README.md b/5-Clustering/2-K-Means/README.md
index ae2475fca1e143204c3a5a92dbca5928a0283d39..deb2037f5db75cd79de618b651a54fe800113524 100644
--- a/5-Clustering/2-K-Means/README.md
+++ b/5-Clustering/2-K-Means/README.md
@@ -4,7 +4,7 @@
> 🎥 Click the image above for a video: Andrew Ng explains clustering
-## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/29/)
+## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/29/)
In this lesson, you will learn how to create clusters using Scikit-learn and the Nigerian music dataset you imported earlier. We will cover the basics of K-Means for Clustering. Keep in mind that, as you learned in the earlier lesson, there are many ways to work with clusters and the method you use depends on your data. We will try K-Means as it's the most common clustering technique. Let's get started!
@@ -238,7 +238,7 @@ Spend some time with this notebook, tweaking parameters. Can you improve the acc
Hint: Try to scale your data. There's commented code in the notebook that adds standard scaling to make the data columns resemble each other more closely in terms of range. You'll find that while the silhouette score goes down, the 'kink' in the elbow graph smooths out. This is because leaving the data unscaled allows data with less variance to carry more weight. Read a bit more on this problem [here](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226).
-## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/30/)
+## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/30/)
## Review & Self Study
diff --git a/5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb b/5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb
index 7a6d69d963b41729db727972b7f92435623abd6e..88461240c61955bd93cd5926f777446638067b14 100644
--- a/5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb
+++ b/5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb
@@ -32,7 +32,7 @@
"source": [
"## Explore K-Means clustering using R and Tidy data principles.\n",
"\n",
- "### [**Pre-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/29/)\n",
+ "### [**Pre-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/29/)\n",
"\n",
"In this lesson, you will learn how to create clusters using the Tidymodels package and other packages in the R ecosystem (we'll call them friends 🧑🤝🧑), and the Nigerian music dataset you imported earlier. We will cover the basics of K-Means for Clustering. Keep in mind that, as you learned in the earlier lesson, there are many ways to work with clusters and the method you use depends on your data. We will try K-Means as it's the most common clustering technique. Let's get started!\n",
"\n",
@@ -593,7 +593,7 @@
"\n",
"Hint: Try to scale your data. There's commented code in the notebook that adds standard scaling to make the data columns resemble each other more closely in terms of range. You'll find that while the silhouette score goes down, the 'kink' in the elbow graph smooths out. This is because leaving the data unscaled allows data with less variance to carry more weight. Read a bit more on this problem [here](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226).\n",
"\n",
- "## [**Post-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/30/)\n",
+ "## [**Post-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/30/)\n",
"\n",
"## **Review & Self Study**\n",
"\n",
diff --git a/5-Clustering/2-K-Means/solution/R/lesson_15.Rmd b/5-Clustering/2-K-Means/solution/R/lesson_15.Rmd
index 2822be2abd49548260f83a2b8342d86b5094f9ea..691262b799b37d58ac4139daa1d03c5743d67ba0 100644
--- a/5-Clustering/2-K-Means/solution/R/lesson_15.Rmd
+++ b/5-Clustering/2-K-Means/solution/R/lesson_15.Rmd
@@ -13,7 +13,7 @@ output:
## Explore K-Means clustering using R and Tidy data principles.
-### [**Pre-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/29/)
+### [**Pre-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/29/)
In this lesson, you will learn how to create clusters using the Tidymodels package and other packages in the R ecosystem (we'll call them friends 🧑🤝🧑), and the Nigerian music dataset you imported earlier. We will cover the basics of K-Means for Clustering. Keep in mind that, as you learned in the earlier lesson, there are many ways to work with clusters and the method you use depends on your data. We will try K-Means as it's the most common clustering technique. Let's get started!
@@ -353,7 +353,7 @@ Spend some time with this notebook, tweaking parameters. Can you improve the acc
Hint: Try to scale your data. There's commented code in the notebook that adds standard scaling to make the data columns resemble each other more closely in terms of range. You'll find that while the silhouette score goes down, the 'kink' in the elbow graph smooths out. This is because leaving the data unscaled allows data with less variance to carry more weight. Read a bit more on this problem [here](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226).
-## [**Post-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/30/)
+## [**Post-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/30/)
## **Review & Self Study**
diff --git a/5-Clustering/2-K-Means/translations/README.es.md b/5-Clustering/2-K-Means/translations/README.es.md
index b9c696e8dcb29816d997277d84e2000347f880c1..3bf18c0837ec8264c5572dc4597914e5b64590e6 100644
--- a/5-Clustering/2-K-Means/translations/README.es.md
+++ b/5-Clustering/2-K-Means/translations/README.es.md
@@ -4,7 +4,7 @@
> 🎥 Haz clic en la imagen de arriba para ver el video: Andrew Ng explica el agrupamiento"
-## [Examen previo a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/29?loc=es)
+## [Examen previo a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/29?loc=es)
En esta lección, aprenderás cómo crear grupos usando Scikit-learn y el conjunto de datos de música Nigeriana que importaste anteriormente. Cubriremos los conceptos básicos de K-Medias para agrupamiento. Ten en mente que, como aprendiste en lecciones anteriores, hay muchas formas de de trabajar con grupos y el método que uses depende de tus datos. Probaremos K-medias ya que es la técnica de agrupamiento más común. ¡Comencemos!
@@ -238,7 +238,7 @@ Dedica algo de tiempo a este notebook, ajustando los parámetros. ¿Puedes mejor
Pista: Prueba escalar tus datos. Hay código comentado en el notebook que agrega escalado estándar para hacer que las columnas de datos se parezcan más entre sí en términos de rango. Encontrarás que mientras el puntaje de silueta disminuye el 'pliegue' en la gráfica de codo se suaviza. Esto es por qué al dejar los datos sin escalar le permite a los datos con menos variación tengan más peso. Lee un poco más de este problema [aquí](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226).
-## [Examen posterior a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/30?loc=es)
+## [Examen posterior a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/30?loc=es)
## Revisión y auto-estudio
diff --git a/5-Clustering/2-K-Means/translations/README.it.md b/5-Clustering/2-K-Means/translations/README.it.md
index 31f60d774346f6e9b3bfda09cc7a8e4d64611445..02829606683b8cc2107b536ec285820920477bcf 100644
--- a/5-Clustering/2-K-Means/translations/README.it.md
+++ b/5-Clustering/2-K-Means/translations/README.it.md
@@ -4,7 +4,7 @@
> 🎥 Fare clic sull'immagine sopra per un video: Andrew Ng spiega il clustering
-## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/29/?loc=it)
+## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/29/?loc=it)
In questa lezione si imparerà come creare cluster utilizzando Scikit-learn e l'insieme di dati di musica nigeriana importato in precedenza. Si tratteranno le basi di K-Means per Clustering. Si tenga presente che, come appreso nella lezione precedente, ci sono molti modi per lavorare con i cluster e il metodo usato dipende dai propri dati. Si proverà K-Means poiché è la tecnica di clustering più comune. Si inizia!
@@ -238,7 +238,7 @@ Trascorrere un po' di tempo con questo notebook, modificando i parametri. E poss
Suggerimento: provare a ridimensionare i dati. C'è un codice commentato nel notebook che aggiunge il ridimensionamento standard per rendere le colonne di dati più simili tra loro in termini di intervallo. Si scoprirà che mentre il punteggio della silhouette diminuisce, il "kink" nel grafico del gomito si attenua. Questo perché lasciare i dati non scalati consente ai dati con meno varianza di avere più peso. Leggere un po' di più su questo problema [qui](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226).
-## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/30/?loc=it)
+## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/30/?loc=it)
## Revisione e Auto Apprendimento
diff --git a/5-Clustering/2-K-Means/translations/README.ko.md b/5-Clustering/2-K-Means/translations/README.ko.md
index d9417d6a372ed589ea70d934c8fcff928793f733..d4ee91c72488c1c22fe7b32db745d9dac149f404 100644
--- a/5-Clustering/2-K-Means/translations/README.ko.md
+++ b/5-Clustering/2-K-Means/translations/README.ko.md
@@ -4,7 +4,7 @@
> 🎥 영상을 보려면 이미지 클릭: Andrew Ng explains clustering
-## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/29/)
+## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/29/)
이 강의에서, Scikit-learn과 함께 이전에 가져온 나이지리아 음악 데이터셋으로 클러스터 제작 방식을 배울 예정입니다. Clustering을 위한 K-Means 기초를 다루게 됩니다. 참고로, 이전 강의에서 배웠던대로, 클러스터로 작업하는 여러 방식이 있고 데이터를 기반한 방식도 있습니다. 가장 일반적 clustering 기술인 K-Means을 시도해보려고 합니다. 시작해봅니다!
@@ -238,7 +238,7 @@ Variance는 "the average of the squared differences from the Mean."으로 정의
힌트: 데이터를 더 키워봅니다. 가까운 범위 조건에 비슷한 데이터 열을 만들고자 추가하는 표준 스케일링 코드를 노트북에 주석으로 남겼습니다. silhouette 점수가 낮아지는 동안, elbow 그래프의 'kink'가 주름 펴지는 것을 볼 수 있습니다. 데이터를 조정하지 않고 남기면 덜 분산된 데이터가 더 많은 가중치로 나를 수 있다는 이유입니다. [here](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226) 이 문제를 조금 더 읽어봅니다.
-## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/30/)
+## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/30/)
## 검토 & 자기주도 학습
diff --git a/5-Clustering/2-K-Means/translations/README.zh-cn.md b/5-Clustering/2-K-Means/translations/README.zh-cn.md
index 7cab44969b6536c300535fb14e58f45e64dc85df..efabf8c1b4ac02b362b4f70d3e1825d0405c058e 100644
--- a/5-Clustering/2-K-Means/translations/README.zh-cn.md
+++ b/5-Clustering/2-K-Means/translations/README.zh-cn.md
@@ -4,7 +4,7 @@
> 🎥 单击上图观看视频：Andrew Ng 解释聚类
-## [课前测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/29/)
+## [课前测验](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/29/)
在本课中，您将学习如何使用 Scikit-learn 和您之前导入的尼日利亚音乐数据集创建聚类。我们将介绍 K-Means 聚类 的基础知识。请记住，正如您在上一课中学到的，使用聚类的方法有很多种，您使用的方法取决于您的数据。我们将尝试 K-Means，因为它是最常见的聚类技术。让我们开始吧！
@@ -239,7 +239,7 @@ K-Means 聚类过程[分三步执行](https://scikit-learn.org/stable/modules/cl
提示：尝试缩放您的数据。笔记本中的注释代码添加了标准缩放，使数据列在范围方面更加相似。您会发现，当轮廓分数下降时，肘部图中的“扭结”变得平滑。这是因为不缩放数据可以让方差较小的数据承载更多的权重。在[这里](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226)阅读更多关于这个问题的[信息](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226)。
-## [课后测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/30/)
+## [课后测验](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/30/)
## 复习与自学
diff --git a/6-NLP/1-Introduction-to-NLP/README.md b/6-NLP/1-Introduction-to-NLP/README.md
index ef7444cb587f53b99d295c265e4111d67f690047..571d7f6855a82c89723f3cba209adc97f7870acb 100644
--- a/6-NLP/1-Introduction-to-NLP/README.md
+++ b/6-NLP/1-Introduction-to-NLP/README.md
@@ -2,7 +2,7 @@
This lesson covers a brief history and important concepts of *natural language processing*, a subfield of *computational linguistics*.
-## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/31/)
+## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/31/)
## Introduction
@@ -149,7 +149,7 @@ Choose one of the "stop and consider" elements above and either try to implement
In the next lesson, you'll learn about a number of other approaches to parsing natural language and machine learning.
-## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/32/)
+## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/32/)
## Review & Self Study
diff --git a/6-NLP/1-Introduction-to-NLP/translations/README.es.md b/6-NLP/1-Introduction-to-NLP/translations/README.es.md
index 620aef1158691cc9d8c76dc24601468bf723ac26..00d156f4fe6cb1cf2fc1d2cb469c3e662a3b01a1 100644
--- a/6-NLP/1-Introduction-to-NLP/translations/README.es.md
+++ b/6-NLP/1-Introduction-to-NLP/translations/README.es.md
@@ -2,7 +2,7 @@
Esta lección cubre una breve historia y conceptos importante del *procesamiento del lenguaje natural*, un subcampo de la *ligüística computacional*.
-## [Examen previo a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/31?loc=es)
+## [Examen previo a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/31?loc=es)
## Introducción
@@ -150,7 +150,7 @@ Elige uno de los elementos "Detente y considera" de arriba y trata de implementa
En la siguiente lección, aprenderás acerca de otros enfoques de cómo analizar el lenguaje natural y aprendizaje automático.
-## [Examen posterior a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/32?loc=es)
+## [Examen posterior a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/32?loc=es)
## Revisión y autoestudio
diff --git a/6-NLP/1-Introduction-to-NLP/translations/README.it.md b/6-NLP/1-Introduction-to-NLP/translations/README.it.md
index 1c96d66498307c1a64e7c5dd808217c068e543b8..938b979cc9c004d76bbbd2b186593ce66d115097 100644
--- a/6-NLP/1-Introduction-to-NLP/translations/README.it.md
+++ b/6-NLP/1-Introduction-to-NLP/translations/README.it.md
@@ -2,7 +2,7 @@
Questa lezione copre una breve storia e concetti importanti dell' *elaborazione del linguaggio naturale*, un sottocampo della *linguistica computazionale*.
-## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/31/?loc=it)
+## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/31/?loc=it)
## Introduzione
@@ -149,7 +149,7 @@ Scegliere uno degli elementi "fermarsi e riflettere" qui sopra e provare a imple
Nella prossima lezione si impareranno una serie di altri approcci all'analisi del linguaggio naturale e dell'machine learning.
-## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/32/?loc=it)
+## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/32/?loc=it)
## Revisione e Auto Apprendimento
diff --git a/6-NLP/1-Introduction-to-NLP/translations/README.ko.md b/6-NLP/1-Introduction-to-NLP/translations/README.ko.md
index 719775ea7f98e68a9d68ea0bf73eb21677b72f3f..bde562c4046f87bfa7a66587337014e5d5134b5a 100644
--- a/6-NLP/1-Introduction-to-NLP/translations/README.ko.md
+++ b/6-NLP/1-Introduction-to-NLP/translations/README.ko.md
@@ -2,7 +2,7 @@
이 강의애서 *computational linguistics* 하위인, *natural language processing*의 간단한 역사와 중요 컨셉을 다룹니다.
-## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/31/)
+## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/31/)
## 소개
@@ -149,7 +149,7 @@ Eliza와 같은, 대화 봇은, 사용자 입력을 유도해서 지능적으로
다음 강의에서, natural language와 머신러닝을 분석하는 여러 다른 접근 방식에 대해 배울 예정입니다.
-## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/32/)
+## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/32/)
## 검토 & 자기주도 학습
diff --git a/6-NLP/1-Introduction-to-NLP/translations/README.pt-br.md b/6-NLP/1-Introduction-to-NLP/translations/README.pt-br.md
index a2ab5cb750a3bca0ce008c67aaed6ece78cc1589..70917c8d6b2c0d63e8d784624569e406ac1430be 100644
--- a/6-NLP/1-Introduction-to-NLP/translations/README.pt-br.md
+++ b/6-NLP/1-Introduction-to-NLP/translations/README.pt-br.md
@@ -2,7 +2,7 @@
Esta aula cobre uma breve história, bem como conceitos importantes do *processamento de linguagem natural*, uma subárea da *Linguística computacional*.
-## [Teste pré-aula](https://white-water-09ec41f0f.azurestaticapps.net/quiz/31?loc=ptbr)
+## [Teste pré-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/31?loc=ptbr)
## Introdução
@@ -157,7 +157,7 @@ Escolha um dos elementos do "pare e considere" acima e tente implementá-lo em c
Na próxima aula, você irá aprender sobre algumas outras abordagens de análise sintática de linguagem natural e de aprendizado de máquina.
-## [Teste pós-aula](https://white-water-09ec41f0f.azurestaticapps.net/quiz/32?loc=ptbr)
+## [Teste pós-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/32?loc=ptbr)
## Revisão & Autoestudo
diff --git a/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md b/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md
index 1252f57eefb13c185ea011968cb77926429b402e..b083d7a9c5311d6bb970927cfaf34872a031e5ef 100644
--- a/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md
+++ b/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md
@@ -1,7 +1,7 @@
# 自然语言处理介绍
这节课讲解了 *自然语言处理* 的简要历史和重要概念，*自然语言处理*是计算语言学的一个子领域。
-## [课前测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/31/)
+## [课前测验](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/31/)
## 介绍
众所周知，自然语言处理（Natural Language Processing, NLP）是机器学习在生产软件中应用最广泛的领域之一。
@@ -147,7 +147,7 @@
在下一课中，您将了解解析自然语言和机器学习的许多其他方法。
-## [课后测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/32/)
+## [课后测验](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/32/)
## 复习与自学
diff --git a/6-NLP/2-Tasks/README.md b/6-NLP/2-Tasks/README.md
index 829df67baa7810cf4ac80db5fb9571a2cf57c22b..a6ee935046e8ed5c76d8fc17306daa936dad8c8f 100644
--- a/6-NLP/2-Tasks/README.md
+++ b/6-NLP/2-Tasks/README.md
@@ -2,7 +2,7 @@
For most *natural language processing* tasks, the text to be processed, must be broken down, examined, and the results stored or cross referenced with rules and data sets. These tasks, allows the programmer to derive the _meaning_ or _intent_ or only the _frequency_ of terms and words in a text.
-## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/33/)
+## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/33/)
Let's discover common techniques used in processing text. Combined with machine learning, these techniques help you to analyse large amounts of text efficiently. Before applying ML to these tasks, however, let's understand the problems encountered by an NLP specialist.
@@ -203,7 +203,7 @@ Implement the bot in the prior knowledge check and test it on a friend. Can it t
Take a task in the prior knowledge check and try to implement it. Test the bot on a friend. Can it trick them? Can you make your bot more 'believable?'
-## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/34/)
+## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/34/)
## Review & Self Study
diff --git a/6-NLP/2-Tasks/translations/README.es.md b/6-NLP/2-Tasks/translations/README.es.md
index dba482ebbc898e17dabeb711983a6721d437e246..02b04ff7a79937f84f7d19afee908b51502bb729 100644
--- a/6-NLP/2-Tasks/translations/README.es.md
+++ b/6-NLP/2-Tasks/translations/README.es.md
@@ -2,7 +2,7 @@
Para la mayoría de tareas de *procesamiento del lenguaje natural*, el texto a ser procesado debe ser partido en bloques, examinado y los resultados almacenados y tener referencias cruzadas con reglas y conjuntos de datos. Esta tareas, le permiten al programador obtener el _significado_, _intención_ o sólo la _frecuencia_ de los términos y palabras en un texto.
-## [Examen previo a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/33?loc=es)
+## [Examen previo a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/33?loc=es)
Descubramos técnicas comunes usadas en el procesamiento de texto. Combinadas con el aprendizaje automático, estas técnicas te ayudan a analizar grandes cantidades de texto de forma eficiente, Antes de aplicar aprendizaje automático a estas tareas, primero entendamos los problemas encontrados por un especialista del procesamiento del lenguaje natural.
@@ -203,7 +203,7 @@ Implementa el bot con la revisión de conocimiento anterior y pruébalo con un a
Toma una tarea de la revisión de conocimiento previo y trata de implementarla. Prueba el bot con un amigo. ¿Pudo engañarlo? ¿Puedes hacer a tu bot más 'creíble'?
-## [Examen posterior a la lectura](https://white-water-09ec41f0f.azurestaticapps.net/quiz/34?loc=es)
+## [Examen posterior a la lectura](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/34?loc=es)
## Revisión y autoestudio
diff --git a/6-NLP/2-Tasks/translations/README.it.md b/6-NLP/2-Tasks/translations/README.it.md
index fe70ccc0361cbf5329654e2ba9a727db46e16a17..f8f274946244d945f1bd740d9fb812a13ce5ab9a 100644
--- a/6-NLP/2-Tasks/translations/README.it.md
+++ b/6-NLP/2-Tasks/translations/README.it.md
@@ -2,7 +2,7 @@
Per la maggior parte delle attività di *elaborazione del linguaggio naturale* , il testo da elaborare deve essere suddiviso, esaminato e i risultati archiviati o incrociati con regole e insiemi di dati. Queste attività consentono al programmatore di derivare il _significato_ o l'_intento_ o solo la _frequenza_ di termini e parole in un testo.
-## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/33/?loc=it)
+## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/33/?loc=it)
Si esaminano le comuni tecniche utilizzate nell'elaborazione del testo. Combinate con machine learning, queste tecniche aiutano ad analizzare grandi quantità di testo in modo efficiente. Prima di applicare machine learning a queste attività, tuttavia, occorre cercare di comprendere i problemi incontrati da uno specialista in NLP.
@@ -203,7 +203,7 @@ Implementare il bot nel controllo delle conoscenze precedenti e testarlo su un a
Prendere un'attività dalla verifica delle conoscenze qui sopra e provare a implementarla. Provare il bot su un amico. Può ingannarlo? Si può rendere il bot più 'credibile?'
-## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/34/?loc=it)
+## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/34/?loc=it)
## Revisione e Auto Apprendimento
diff --git a/6-NLP/2-Tasks/translations/README.ko.md b/6-NLP/2-Tasks/translations/README.ko.md
index ac6c7feb570f502d55d764ca29ad486ad6abc159..de8e77925fea8e9ca53f8390f11932c01c600a46 100644
--- a/6-NLP/2-Tasks/translations/README.ko.md
+++ b/6-NLP/2-Tasks/translations/README.ko.md
@@ -2,7 +2,7 @@
대부분 *natural language processing* 작업으로, 처리한 텍스트를 분해하고, 검사하고, 그리고 결과를 저장하거나 룰과 데이터셋을 서로 참조했습니다. 이 작업들로, 프로그래머가 _meaning_ 또는 _intent_ 또는 오직 텍스트에 있는 용어와 단어의 _frequency_ 만 끌어낼 수 있게 합니다.
-## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/33/)
+## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/33/)
텍스트를 처리하며 사용했던 일반적인 기술을 찾아봅니다. 머신러닝에 결합된, 이 기술은 효율적으로 많은 텍스트를 분석하는데 도와줍니다. 그러나, 이 작업에 ML을 적용하기 전에, NLP 스페셜리스트가 일으킨 문제를 이해합니다.
@@ -203,7 +203,7 @@ It was nice talking to you, goodbye!
이전의 지식 점검에서 작업하고 구현합니다. 친구에게 봇을 테스트합니다. 그들을 속일 수 있나요? 좀 더 '믿을 수'있게 봇을 만들 수 있나요?
-## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/34/)
+## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/34/)
## 검토 & 자기주도 학습
diff --git a/6-NLP/2-Tasks/translations/README.pt-br.md b/6-NLP/2-Tasks/translations/README.pt-br.md
index 39ebe291176e4e80d1c2b0c2222125b92d9fde20..6df96effb000b74746c97462c8910d040063a264 100644
--- a/6-NLP/2-Tasks/translations/README.pt-br.md
+++ b/6-NLP/2-Tasks/translations/README.pt-br.md
@@ -2,7 +2,7 @@
Para a maioria das tarefas de *processamento de linguagem natural*, o texto a ser processado precisa ser quebrado em partes e examinado, e os resultados precisam ser guardados ou cruzados com regras e data sets. Estas tarefas permitem que o programador obtenha _significado_, _intencionalidade_ ou a _frequência_ de termos e palavras em um texto.
-## [Teste pré-aula](https://white-water-09ec41f0f.azurestaticapps.net/quiz/33?loc=ptbr)
+## [Teste pré-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/33?loc=ptbr)
Vamos descobrir técnicas frequentemente usadas no processamento de texto. Combinadas com aprendizado de máquina, estas técnicas ajudam você a analisar grandes quantidades de texto com eficiência. Contudo, antes de aplicar o aprendizado de máquina para estas tarefas, vamos entender os problemas enfrentados por um especialista de PLN (ou NLP).
@@ -209,7 +209,7 @@ Uma possível resposta para a tarefa está [aqui](../solution/bot.py)
Implemente o bot discutido acima da seção checagem de conhecimento e teste-o em amigos. O bot consegue enganá-los? Você consegue fazer seu bot mais convincente?
-## [Teste pós-aula](https://white-water-09ec41f0f.azurestaticapps.net/quiz/34?loc=ptbr)
+## [Teste pós-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/34?loc=ptbr)
## Revisão & Autoestudo
diff --git a/6-NLP/3-Translation-Sentiment/README.md b/6-NLP/3-Translation-Sentiment/README.md
index e03c48b31ca039a69cfff186d7a455eda7685af8..5415b77454364a48b809aca870cbfae315c476c3 100644
--- a/6-NLP/3-Translation-Sentiment/README.md
+++ b/6-NLP/3-Translation-Sentiment/README.md
@@ -2,7 +2,7 @@
In the previous lessons you learned how to build a basic bot using `TextBlob`, a library that embeds ML behind-the-scenes to perform basic NLP tasks such as noun phrase extraction. Another important challenge in computational linguistics is accurate _translation_ of a sentence from one spoken or written language to another.
-## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/35/)
+## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/35/)
Translation is a very hard problem compounded by the fact that there are thousands of languages and each can have very different grammar rules. One approach is to convert the formal grammar rules for one language, such as English, into a non-language dependent structure, and then translate it by converting back to another language. This approach means that you would take the following steps:
@@ -176,7 +176,7 @@ Here is a sample [solution](solution/notebook.ipynb).
Can you make Marvin even better by extracting other features from the user input?
-## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/36/)
+## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/36/)
## Review & Self Study
diff --git a/6-NLP/3-Translation-Sentiment/translations/README.es.md b/6-NLP/3-Translation-Sentiment/translations/README.es.md
index 501f4e58cd439677de3429697d36c7c9e283ea7e..c86d9bef5fb210a0eebb7b717ca16ab645e8c94c 100644
--- a/6-NLP/3-Translation-Sentiment/translations/README.es.md
+++ b/6-NLP/3-Translation-Sentiment/translations/README.es.md
@@ -2,7 +2,7 @@
En las lecciones anteriores aprendiste cómo construir un bot básico usando `TextBlob`, una biblioteca que embebe aprendizaje automático tras bambalinas para realizar tareas básicas de procesamiento del lenguaje natural (NLP) tales como extracción de frases nominales. Otro desafío importante en la lingüística computacional es la _traducción_ precisa de una oración de un idioma hablado o escrito a otro.
-## [Examen previo a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/35?loc=es)
+## [Examen previo a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/35?loc=es)
La traducción es siempre un problema difícil compuesto por el hecho que existen miles de idiomas y cada uno puede tener distintas reglas gramaticales. Un enfoque es convertir las reglas gramaticales formales para un idioma, como el Inglés, a una estructura no dependiente del idioma, y luego traducirlo al convertirlo de nuevo a otro idioma. Este enfoque significa que deberías realizar los siguientes pasos:
@@ -176,7 +176,7 @@ Aquí tienes una [solución de muestra](../solution/notebook.ipynb).
¿Puedes hacer a Marvin aún mejor al extraer otras características de la entrada del usuario?
-## [Examen posterior a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/36?loc=es)
+## [Examen posterior a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/36?loc=es)
## Revisión y autoestudio
diff --git a/6-NLP/3-Translation-Sentiment/translations/README.it.md b/6-NLP/3-Translation-Sentiment/translations/README.it.md
index 298939894c1aaa26c830e03a939024268838b83c..7f82d7668695369ab51a622024626e5966cf0fad 100644
--- a/6-NLP/3-Translation-Sentiment/translations/README.it.md
+++ b/6-NLP/3-Translation-Sentiment/translations/README.it.md
@@ -2,7 +2,7 @@
Nelle lezioni precedenti si è imparato come creare un bot di base utilizzando `TextBlob`, una libreria che incorpora machine learning dietro le quinte per eseguire attività di base di NPL come l'estrazione di frasi nominali. Un'altra sfida importante nella linguistica computazionale è _la traduzione_ accurata di una frase da una lingua parlata o scritta a un'altra.
-## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/35/?loc=it)
+## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/35/?loc=it)
La traduzione è un problema molto difficile, aggravato dal fatto che ci sono migliaia di lingue e ognuna può avere regole grammaticali molto diverse. Un approccio consiste nel convertire le regole grammaticali formali per una lingua, come l'inglese, in una struttura non dipendente dalla lingua e quindi tradurla convertendola in un'altra lingua. Questo approccio significa che si dovrebbero eseguire i seguenti passaggi:
@@ -176,7 +176,7 @@ Ecco una [soluzione](../solution/notebook.ipynb) di esempio.
Si può rendere Marvin ancora migliore estraendo altre funzionalità dall'input dell'utente?
-## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/36/?loc=it)
+## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/36/?loc=it)
## Revisione e Auto Apprendimento
diff --git a/6-NLP/3-Translation-Sentiment/translations/README.ko.md b/6-NLP/3-Translation-Sentiment/translations/README.ko.md
index c9c6526e67dcaa31b38ae0e3b3dc9ced501bb49f..a18dee6e8a5d93abb4a3806febb8f9fb0f536abc 100644
--- a/6-NLP/3-Translation-Sentiment/translations/README.ko.md
+++ b/6-NLP/3-Translation-Sentiment/translations/README.ko.md
@@ -2,7 +2,7 @@
이전 강의에서 noun phrase 추출하는 기초 NLP 작업을 하기 위해 ML behind-the-scenes을 포함한 라이브러리인, `TextBlob`으로 기본적인 봇을 만드는 방식을 배웠습니다. 컴퓨터 언어학에서 다른 중요한 도전은 구두나 다른 언어로 문장을 정확하게 _translation_ 하는 것입니다.
-## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/35/)
+## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/35/)
번역은 천여 개 언어와 각자 많이 다른 문법 규칙이 있다는 사실에 의해서 합쳐진 매우 어려운 문제입니다. 한 접근 방식은 영어처럼, 한 언어의 형식적인 문법 규칙을 비-언어 종속 구조로 변환하고, 다른 언어로 변환하면서 번역합니다. 이 접근 방식은 다음 단계로 진행된다는 점을 의미합니다:
@@ -177,7 +177,7 @@ Darcy, as well as Elizabeth, really loved them; and they were
사용자 입력으로 다른 features를 추출해서 Marvin을 더 좋게 만들 수 있나요?
-## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/36/)
+## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/36/)
## 검토 & 자기주도 학습
diff --git a/6-NLP/4-Hotel-Reviews-1/README.md b/6-NLP/4-Hotel-Reviews-1/README.md
index 393022aef045dc550650167f4cee7a566a12ace8..77a9537e5840a6d20ca13258af556669bfb80ccf 100644
--- a/6-NLP/4-Hotel-Reviews-1/README.md
+++ b/6-NLP/4-Hotel-Reviews-1/README.md
@@ -6,7 +6,7 @@ In this section you will use the techniques in the previous lessons to do some e
- how to calculate some new data based on the existing columns
- how to save the resulting dataset for use in the final challenge
-## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/37/)
+## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/37/)
### Introduction
@@ -393,7 +393,7 @@ Now that you have explored the dataset, in the next lesson you will filter the d
This lesson demonstrates, as we saw in previous lessons, how critically important it is to understand your data and its foibles before performing operations on it. Text-based data, in particular, bears careful scrutiny. Dig through various text-heavy datasets and see if you can discover areas that could introduce bias or skewed sentiment into a model.
-## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/38/)
+## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/38/)
## Review & Self Study
diff --git a/6-NLP/4-Hotel-Reviews-1/translations/README.es.md b/6-NLP/4-Hotel-Reviews-1/translations/README.es.md
index 39c6ed52bb3997fe339ed72034188ccdda10f3d5..8c478465c83a0486aca87cf0f4d59d888fed2d07 100644
--- a/6-NLP/4-Hotel-Reviews-1/translations/README.es.md
+++ b/6-NLP/4-Hotel-Reviews-1/translations/README.es.md
@@ -6,7 +6,7 @@ En esta sección usarás las técnicas de las lecciones anteriores para hacer un
- cómo calcular algunos datos nuevos basándote en las columnas existentes
- cómo guardar el conjunto de datos resultante para usarlo en el desafío final
-## [Examen previo a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/37?loc=es)
+## [Examen previo a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/37?loc=es)
### Introducción
@@ -404,7 +404,7 @@ Ahora que has explorado el conjunto de datos, en la próxima lección filtrarás
Esta lección demuestra, como vimos en lecciones anteriores, qué tan críticamente importante es entender tus datos y sus imperfecciones antes de realizar operaciones sobre ellos. Los datos basados en texto, requieren particularmente un minucioso escrutinio. Profundiza en grandes conjuntos de datos basados en texto y ve si puedes descubrir áreas que podrían presentar sesgos o sentimientos sesgados en un modelo.
-## [Examen posterior a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/38?loc=es)
+## [Examen posterior a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/38?loc=es)
## Revisión y autoestudio
diff --git a/6-NLP/4-Hotel-Reviews-1/translations/README.it.md b/6-NLP/4-Hotel-Reviews-1/translations/README.it.md
index 10622683a671dcdd6236a9ef507bbff786077ec9..47c36eb36414371284941973d6f1949cfae862be 100644
--- a/6-NLP/4-Hotel-Reviews-1/translations/README.it.md
+++ b/6-NLP/4-Hotel-Reviews-1/translations/README.it.md
@@ -6,7 +6,7 @@ In questa sezione si utilizzeranno le tecniche delle lezioni precedenti per eseg
- come calcolare alcuni nuovi dati in base alle colonne esistenti
- come salvare l'insieme di dati risultante per l'uso nella sfida finale
-## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/37/?loc=it)
+## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/37/?loc=it)
### Introduzione
@@ -401,7 +401,7 @@ Ora che si è esplorato l'insieme di dati, nella prossima lezione si filtreranno
Questa lezione dimostra, come visto nelle lezioni precedenti, quanto sia di fondamentale importanza comprendere i dati e le loro debolezze prima di eseguire operazioni su di essi. I dati basati su testo, in particolare, sono oggetto di un attento esame. Esaminare vari insiemi di dati contenenti principalmente testo e vedere se si riesce a scoprire aree che potrebbero introdurre pregiudizi o sentiment distorti in un modello.
-## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/38/?loc=it)
+## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/38/?loc=it)
## Revisione e Auto Apprendimento
diff --git a/6-NLP/4-Hotel-Reviews-1/translations/README.ko.md b/6-NLP/4-Hotel-Reviews-1/translations/README.ko.md
index 59518abdc9c7afb537b734d13f275da3c83b334a..f2b2852fd8162abb0ab9cda5b4328bebd193a607 100644
--- a/6-NLP/4-Hotel-Reviews-1/translations/README.ko.md
+++ b/6-NLP/4-Hotel-Reviews-1/translations/README.ko.md
@@ -6,7 +6,7 @@
- 이미 존재하는 열을 기반으로 일부 새로운 데이터를 계산하는 방식
- 최종 도전에서 사용하고자 결과 데이터셋을 저장하는 방식
-## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/37/)
+## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/37/)
### 소개
@@ -397,7 +397,7 @@ print("Loading took " + str(round(end - start, 2)) + " seconds")
이전 강의에서 본 것처럼, 이 강의에서 작업하기 전 데이터와 약점을 이해하는 것이 얼마나 치명적이게 중요한지 보여줍니다. 특별히, 텍스트-기반 데이터는, 조심히 조사해야 합니다. 다양한 text-heavy 데이터셋을 파보고 모델에서 치우치거나 편향된 감정으로 끼워놓은 영역을 찾을 수 있는지 확인합니다.
-## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/38/)
+## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/38/)
## 검토 & 자기주도 학습
diff --git a/6-NLP/5-Hotel-Reviews-2/README.md b/6-NLP/5-Hotel-Reviews-2/README.md
index b8bbddd803d2e54c43de031e2ef670628eced26f..092ff88a27d26e5dac0ebf44d323c2a750ba3e74 100644
--- a/6-NLP/5-Hotel-Reviews-2/README.md
+++ b/6-NLP/5-Hotel-Reviews-2/README.md
@@ -1,7 +1,7 @@
# Sentiment analysis with hotel reviews
Now that you have explored the dataset in detail, it's time to filter the columns and then use NLP techniques on the dataset to gain new insights about the hotels.
-## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/39/)
+## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/39/)
### Filtering & Sentiment Analysis Operations
@@ -360,7 +360,7 @@ To review, the steps are:
When you started, you had a dataset with columns and data but not all of it could be verified or used. You've explored the data, filtered out what you don't need, converted tags into something useful, calculated your own averages, added some sentiment columns and hopefully, learned some interesting things about processing natural text.
-## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/40/)
+## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/40/)
## Challenge
diff --git a/6-NLP/5-Hotel-Reviews-2/translations/README.es.md b/6-NLP/5-Hotel-Reviews-2/translations/README.es.md
index bbd3296c82aba01b1bfa8102be4bdbd476c94b9a..28e35d304714780cde717a83400f10421623f17a 100644
--- a/6-NLP/5-Hotel-Reviews-2/translations/README.es.md
+++ b/6-NLP/5-Hotel-Reviews-2/translations/README.es.md
@@ -2,7 +2,7 @@
Ahora que has explorado a detalle el conjunto de datos, es momento de filtrar las columnas y luego usar técnicas de procesamiento del lenguaje natural sobre el conjunto de datos para obtener nuevos conocimientos acerca de los hoteles.
-## [Examen previo a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/39?loc=es)
+## [Examen previo a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/39?loc=es)
### Filtrado y operaciones de análisis de sentimiento
@@ -361,7 +361,7 @@ Para revisar, los pasos son:
Cuando iniciaste, tenías un conjunto de datos con columnas y datos pero no todos ello podían ser verificados o usados. Exploraste los datos, filtraste lo que no necesitas, convertiste etiquetas en algo útil, calculaste tus propios promedios, agregaste algunas columnas de sentimiento y espero hayas aprendido cosas interesantes acerca de procesar texto natural.
-## [Examen posterior a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/40?loc=es)
+## [Examen posterior a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/40?loc=es)
## Desafío
diff --git a/6-NLP/5-Hotel-Reviews-2/translations/README.it.md b/6-NLP/5-Hotel-Reviews-2/translations/README.it.md
index 2e7780003a2f3e52c6ea42e204067fde8950e3f4..6e62cd9a3ab1268e465535ee5fe172ae25dc3350 100644
--- a/6-NLP/5-Hotel-Reviews-2/translations/README.it.md
+++ b/6-NLP/5-Hotel-Reviews-2/translations/README.it.md
@@ -2,7 +2,7 @@
Ora che si è esplorato in dettaglio l'insieme di dati, è il momento di filtrare le colonne e quindi utilizzare le tecniche NLP sull'insieme di dati per ottenere nuove informazioni sugli hotel.
-## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/39/?loc=it)
+## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/39/?loc=it)
### Operazioni di Filtraggio e Analisi del Sentiment
@@ -361,7 +361,7 @@ Per riepilogare, i passaggi sono:
Quando si è iniziato, si disponeva di un insieme di dati con colonne e dati, ma non tutto poteva essere verificato o utilizzato. Si sono esplorati i dati, filtrato ciò che non serve, convertito i tag in qualcosa di utile, calcolato le proprie medie, aggiunto alcune colonne di sentiment e, si spera, imparato alcune cose interessanti sull'elaborazione del testo naturale.
-## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/40/?loc=it)
+## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/40/?loc=it)
## Sfida
diff --git a/6-NLP/5-Hotel-Reviews-2/translations/README.ko.md b/6-NLP/5-Hotel-Reviews-2/translations/README.ko.md
index da892099302e23099e622706006240b7689668e0..aeaa0b2de082ca3db603407944602354ae234523 100644
--- a/6-NLP/5-Hotel-Reviews-2/translations/README.ko.md
+++ b/6-NLP/5-Hotel-Reviews-2/translations/README.ko.md
@@ -2,7 +2,7 @@
지금까지 자세히 데이터셋을 살펴보았으며, 열을 필터링하고 데이터셋으로 NLP 기술을 사용하여 호텔에 대한 새로운 시각을 얻게 될 시간입니다.
-## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/39/)
+## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/39/)
### 필터링 & 감정 분석 작업
@@ -361,7 +361,7 @@ df.to_csv(r"../data/Hotel_Reviews_NLP.csv", index = False)
시작했을 때, 열과 데이터로 이루어진 데이터셋이 었었지만 모두 다 확인되거나 사용되지 않았습니다. 데이터를 살펴보았으며, 필요없는 것은 필터링해서 지웠고, 유용하게 태그를 변환했고, 평균을 계산했으며, 일부 감정 열을 추가하고 기대하면서, 자연어 처리에 대한 일부 흥미로운 사실을 학습했습니다.
-## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/40/)
+## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/40/)
## 도전
diff --git a/7-TimeSeries/1-Introduction/README.md b/7-TimeSeries/1-Introduction/README.md
index de17f4547f1953b23fd34bbb4dc5c1a2ded9ed1f..e875fdc94945c9116059212168772ccc9c8eb649 100644
--- a/7-TimeSeries/1-Introduction/README.md
+++ b/7-TimeSeries/1-Introduction/README.md
@@ -10,7 +10,7 @@ In this lesson and the following one, you will learn a bit about time series for
> 🎥 Click the image above for a video about time series forecasting
-## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/41/)
+## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/41/)
It's a useful and interesting field with real value to business, given its direct application to problems of pricing, inventory, and supply chain issues. While deep learning techniques have started to be used to gain more insights to better predict future performance, time series forecasting remains a field greatly informed by classic ML techniques.
@@ -174,7 +174,7 @@ In the next lesson, you will create an ARIMA model to create some forecasts.
Make a list of all the industries and areas of inquiry you can think of that would benefit from time series forecasting. Can you think of an application of these techniques in the arts? In Econometrics? Ecology? Retail? Industry? Finance? Where else?
-## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/42/)
+## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/42/)
## Review & Self Study
diff --git a/7-TimeSeries/1-Introduction/translations/README.es.md b/7-TimeSeries/1-Introduction/translations/README.es.md
index 282dd328235b5c76781b989884607fedba48953b..04a88a9903526bee67ee5b7543bca578544c42cc 100644
--- a/7-TimeSeries/1-Introduction/translations/README.es.md
+++ b/7-TimeSeries/1-Introduction/translations/README.es.md
@@ -10,7 +10,7 @@ En esta lección y la siguiente, aprenderás un poco acerca de la predicción de
> 🎥 Da clic en la imagen de arriba para ver un video acerca de la predicción de series de tiempo
-## [Examen previo a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/41?loc=es)
+## [Examen previo a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/41?loc=es)
Es un campo útil e interesante con valor real para el negocio, dada su aplicación directa a problemas de precio, inventario e incidentes de cadenas de suministro. Mientras que las técnicas de aprendizaje profundo han comenzado a usarse para ganar más conocimiento para mejorar el rendimiento de futuras predicciones, la predicción de series de tiempo sigue siendo un campo muy informado por técnicas de aprendizaje automático clásico.
@@ -175,7 +175,7 @@ En la siguiente lección, crearás un modelo ARIMA para realizar algunas predicc
Haz una lista de todas las industrias y áreas de consulta en las que puedes pensar que se beneficiarían de la predicción de series de tiempo. ¿Puedes pensar en una aplicación de estas técnicas en las artes, en la econometría, ecología, venta al menudeo, la industria, finanzas? ¿Dónde más?
-## [Examen posterior a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/42?loc=es)
+## [Examen posterior a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/42?loc=es)
## Revisión y autoestudio
diff --git a/7-TimeSeries/1-Introduction/translations/README.it.md b/7-TimeSeries/1-Introduction/translations/README.it.md
index 9c7830d9008dd34e3401192bac8494b1f0485a42..5d4f49a48e4384801a1c759f0a9ad01a4bef5bc8 100644
--- a/7-TimeSeries/1-Introduction/translations/README.it.md
+++ b/7-TimeSeries/1-Introduction/translations/README.it.md
@@ -10,7 +10,7 @@ In questa lezione e nella successiva si imparerà qualcosa sulla previsione dell
> 🎥 Fare clic sull'immagine sopra per un video sulla previsione delle serie temporali
-## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/41/?loc=it)
+## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/41/?loc=it)
È un campo utile e interessante con un valore reale per il business, data la sua applicazione diretta a problemi di prezzi, inventario e problemi della catena di approvvigionamento. Mentre le tecniche di deep learning hanno iniziato a essere utilizzate per acquisire maggiori informazioni per prevedere meglio le prestazioni future, la previsione delle serie temporali rimane un campo ampiamente informato dalle tecniche classiche di ML.
@@ -174,7 +174,7 @@ Nella prossima lezione, si creerà un modello ARIMA per creare alcune previsioni
Fare un elenco di tutti i settori e le aree di indagine che vengono in mente che potrebbero trarre vantaggio dalla previsione delle serie temporali. Si riesce a pensare a un'applicazione di queste tecniche nelle arti? In Econometria? Ecologia? Vendita al Dettaglio? Industria? Finanza? Dove se no?
-## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/42/?loc=it)
+## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/42/?loc=it)
## Revisione e Auto Apprendimento
diff --git a/7-TimeSeries/1-Introduction/translations/README.ko.md b/7-TimeSeries/1-Introduction/translations/README.ko.md
index d8f04e2220cc82f9ef9d8728dd003d4b6f1dd5d6..a096a6e25f8ce7bc499c5370d1968c9ec5a4ef95 100644
--- a/7-TimeSeries/1-Introduction/translations/README.ko.md
+++ b/7-TimeSeries/1-Introduction/translations/README.ko.md
@@ -10,7 +10,7 @@
> 🎥 이미지를 눌러서 time series forecasting에 대한 비디오를 봅니다
-## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/41/)
+## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/41/)
가격, 재고, 그리고 공급과 연관된 이슈에 직접 적용하게 된다면, 비지니스에 실제로 가치있는 유용하고 흥미로운 필드가 됩니다. 딥러닝 기술은 미래의 성능을 잘 예측하기 위해 더 많은 인사이트를 얻고자 사용했지만, time series forecasting은 classic ML 기술에서 지속적으로 많은 정보를 얻는 필드입니다.
@@ -175,7 +175,7 @@ seasonality의 독립적으로, 1년 보다 긴 경제 침체같은 long-run cyc
time series forecasting에서 얻을 수 있다고 생각할 수 있는 모든 산업과 조사 영역의 리스트를 만듭니다. 예술에 이 기술을 적용할 수 있다고 생각하나요? 경제학에서? 생태학에서? 리테일에서? 산업에서? 금융에서? 또 다른 곳은 어딘가요?
-## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/42/)
+## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/42/)
## 검토 & 자기주도 학습
diff --git a/7-TimeSeries/2-ARIMA/README.md b/7-TimeSeries/2-ARIMA/README.md
index 19da5622169e02c8686719d03e5884020c14988c..b421a446f15baacb6f9dfa4fabeb08e60f07d56e 100644
--- a/7-TimeSeries/2-ARIMA/README.md
+++ b/7-TimeSeries/2-ARIMA/README.md
@@ -6,7 +6,7 @@ In the previous lesson, you learned a bit about time series forecasting and load
> 🎥 Click the image above for a video: A brief introduction to ARIMA models. The example is done in R, but the concepts are universal.
-## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/43/)
+## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/43/)
## Introduction
@@ -383,7 +383,7 @@ Check the accuracy of your model by testing its mean absolute percentage error (
Dig into the ways to test the accuracy of a Time Series Model. We touch on MAPE in this lesson, but are there other methods you could use? Research them and annotate them. A helpful document can be found [here](https://otexts.com/fpp2/accuracy.html)
-## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/44/)
+## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/44/)
## Review & Self Study
diff --git a/7-TimeSeries/2-ARIMA/translations/README.it.md b/7-TimeSeries/2-ARIMA/translations/README.it.md
index 0b08ee57b3bc1dd941da3dbe32cb2cfccc4197f6..6100b0bc21778cb1910ed919ae71ec260780cc2a 100644
--- a/7-TimeSeries/2-ARIMA/translations/README.it.md
+++ b/7-TimeSeries/2-ARIMA/translations/README.it.md
@@ -6,7 +6,7 @@ Nella lezione precedente, si è imparato qualcosa sulla previsione delle serie t
> 🎥 Fare clic sull'immagine sopra per un video: Una breve introduzione ai modelli ARIMA. L'esempio è fatto in linguaggio R, ma i concetti sono universali.
-## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/43/?loc=it)
+## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/43/?loc=it)
## Introduzione
@@ -383,7 +383,7 @@ Controllare l'accuratezza del modello testando il suo errore percentuale medio a
Scoprire i modi per testare l'accuratezza di un modello di serie temporali. Si esamina MAPE in questa lezione, ma ci sono altri metodi che si potrebbero usare? Ricercarli e annotarli. Un documento utile può essere trovato [qui](https://otexts.com/fpp2/accuracy.html)
-## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/44/?loc=it)
+## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/44/?loc=it)
## Revisione e Auto Apprendimento
diff --git a/7-TimeSeries/2-ARIMA/translations/README.ko.md b/7-TimeSeries/2-ARIMA/translations/README.ko.md
index 7ed625539f61184480c2ddf0ffd36eba4bc3715d..030d418dd56f7018bee39b53eb68c790358a7652 100644
--- a/7-TimeSeries/2-ARIMA/translations/README.ko.md
+++ b/7-TimeSeries/2-ARIMA/translations/README.ko.md
@@ -6,7 +6,7 @@
> 🎥 영상을 보려면 이미지 클릭: A brief introduction to ARIMA models. The example is done in R, but the concepts are universal.
-## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/43/)
+## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/43/)
## 소개
@@ -383,7 +383,7 @@ Walk-forward 검사는 time series 모델 평가의 최적 표준이고 이 프
Time Series 모델의 정확도를 테스트할 방식을 파봅니다. 이 강의에서 MAPE을 다루지만, 사용할 다른 방식이 있나요? 조사해보고 첨언해봅니다. 도움을 받을 수 있는 문서는 [here](https://otexts.com/fpp2/accuracy.html)에서 찾을 수 있습니다.
-## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/44/)
+## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/44/)
## 검토 & 자기주도 학습
diff --git a/7-TimeSeries/3-SVR/README.md b/7-TimeSeries/3-SVR/README.md
index 4f55894cc9b4120e62e1241a24a6843e3d7d9e15..5fcb871946cb98eb624d1031d910502a659f8ac4 100644
--- a/7-TimeSeries/3-SVR/README.md
+++ b/7-TimeSeries/3-SVR/README.md
@@ -2,7 +2,7 @@
In the previous lesson, you learned how to use ARIMA model to make time series predictions. Now you'll be looking at Support Vector Regressor model which is a regressor model used to predict continuous data.
-## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/51/)
+## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/51/)
## Introduction
@@ -367,7 +367,7 @@ MAPE: 2.0572089029888656 %
- Try to use different kernel functions for the model and analyze their performances on the dataset. A helpful document can be found [here](https://scikit-learn.org/stable/modules/svm.html#kernel-functions).
- Try using different values for `timesteps` for the model to look back to make prediction.
-## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/52/)
+## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/52/)
## Review & Self Study
diff --git a/8-Reinforcement/1-QLearning/README.md b/8-Reinforcement/1-QLearning/README.md
index 52aecd0bab8b07398f709951a7d0f3a7ff08cae3..2e207429789b98b4b8c32ae32faad321fbea1585 100644
--- a/8-Reinforcement/1-QLearning/README.md
+++ b/8-Reinforcement/1-QLearning/README.md
@@ -11,7 +11,7 @@ By using reinforcement learning and a simulator (the game), you can learn how to
> 🎥 Click the image above to hear Dmitry discuss Reinforcement Learning
-## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/45/)
+## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/45/)
## Prerequisites and Setup
@@ -314,6 +314,6 @@ The learnings can be summarized as:
Overall, it is important to remember that the success and quality of the learning process significantly depends on parameters, such as learning rate, learning rate decay, and discount factor. Those are often called **hyperparameters**, to distinguish them from **parameters**, which we optimize during training (for example, Q-Table coefficients). The process of finding the best hyperparameter values is called **hyperparameter optimization**, and it deserves a separate topic.
-## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/46/)
+## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/46/)
## Assignment [A More Realistic World](assignment.md)
diff --git a/8-Reinforcement/1-QLearning/translations/README.it.md b/8-Reinforcement/1-QLearning/translations/README.it.md
index 9d91cdece8fcdaae6ebc61a5fb79a7f1deb61b68..a0576ea2cd443a069c65904a6c39e335346b308a 100644
--- a/8-Reinforcement/1-QLearning/translations/README.it.md
+++ b/8-Reinforcement/1-QLearning/translations/README.it.md
@@ -11,7 +11,7 @@ Usando reinforcement learning e un simulatore (il gioco), si può imparare a gio
> 🎥 Fare clic sull'immagine sopra per ascoltare Dmitry discutere sul reinforcement learning
-## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/45/?loc=it)
+## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/45/?loc=it)
## Prerequisiti e Configurazione
@@ -315,6 +315,6 @@ Gli apprendimenti possono essere riassunti come:
Nel complesso, è importante ricordare che il successo e la qualità del processo di apprendimento dipendono in modo significativo da parametri come il tasso di apprendimento, il decadimento del tasso di apprendimento e il fattore di sconto. Questi sono spesso chiamati **iperparametri**, per distinguerli dai **parametri**, che si ottimizzano durante l'allenamento (ad esempio, i coefficienti della Q-Table). Il processo per trovare i valori migliori degli iperparametri è chiamato **ottimizzazione degli iperparametri** e merita un argomento a parte.
-## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/46/?loc=fr)
+## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/46/?loc=fr)
## Incarico: [Un mondo più realistico](assignment.it.md)
diff --git a/8-Reinforcement/1-QLearning/translations/README.ko.md b/8-Reinforcement/1-QLearning/translations/README.ko.md
index b91313b67f1cf032723dc2b1d4ddedcabf34771b..b990ed5a3b60d9622348c9990f147376cadb0c45 100644
--- a/8-Reinforcement/1-QLearning/translations/README.ko.md
+++ b/8-Reinforcement/1-QLearning/translations/README.ko.md
@@ -11,7 +11,7 @@ reinforcement learning과 (게임) 시뮬레이터로, 살아남고 가능한
> 🎥 Dmitry discuss Reinforcement Learning 들으려면 이미지 클릭
-## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/45/)
+## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/45/)
## 전제조건 및 설정
@@ -315,7 +315,7 @@ print_statistics(qpolicy)
전체적으로, 학습 프로세스의 성공과 퀄리티는 학습률, 학습률 감소, 그리고 감가율처럼 파라미터에 기반하는게 상당히 중요하다는 점을 기억합니다. 훈련하면서 최적화하면 (예시로, Q-Table coefficients), **parameters**와 구별해서, 가끔 **hyperparameters**라고 불립니다. 최고의 hyperparameter 값을 찾는 프로세스는 **hyperparameter optimization**이라고 불리며, 별도의 토픽이 있을 만합니다.
-## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/46/)
+## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/46/)
## 과제
diff --git a/8-Reinforcement/1-QLearning/translations/README.zh-cn.md b/8-Reinforcement/1-QLearning/translations/README.zh-cn.md
index 735d78b2df61e324448c547146950b803e957ee5..fc0479cadcc61a3d96938c0a9aea4e9cec248540 100644
--- a/8-Reinforcement/1-QLearning/translations/README.zh-cn.md
+++ b/8-Reinforcement/1-QLearning/translations/README.zh-cn.md
@@ -11,7 +11,7 @@
> 🎥 点击上图观看 Dmitry 讨论强化学习
-## [课前测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/45/)
+## [课前测验](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/45/)
## 先决条件和设置
@@ -315,6 +315,6 @@ print_statistics(qpolicy)
总的来说，重要的是要记住学习过程的成功和质量在很大程度上取决于参数，例如学习率、学习率衰减和折扣因子。这些通常称为**超参数**，以区别于我们在训练期间优化的**参数**（例如，Q-Table 系数）。寻找最佳超参数值的过程称为**超参数优化**，它值得一个单独的话题来介绍。
-## [课后测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/46/)
+## [课后测验](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/46/)
## 作业[一个更真实的世界](assignment.zh-cn.md)
diff --git a/8-Reinforcement/2-Gym/README.md b/8-Reinforcement/2-Gym/README.md
index 6331cfc636355989b6465aab0e1ad7e16e260612..7faea6e00db6a493a5341798b278c25be59a8275 100644
--- a/8-Reinforcement/2-Gym/README.md
+++ b/8-Reinforcement/2-Gym/README.md
@@ -2,7 +2,7 @@
The problem we have been solving in the previous lesson might seem like a toy problem, not really applicable for real life scenarios. This is not the case, because many real world problems also share this scenario - including playing Chess or Go. They are similar, because we also have a board with given rules and a **discrete state**.
https://white-water-09ec41f0f.azurestaticapps.net/
-## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/47/)
+## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/47/)
## Introduction
@@ -329,7 +329,7 @@ You should see something like this:
> **Task 4**: Here we were not selecting the best action on each step, but rather sampling with corresponding probability distribution. Would it make more sense to always select the best action, with the highest Q-Table value? This can be done by using `np.argmax` function to find out the action number corresponding to highers Q-Table value. Implement this strategy and see if it improves the balancing.
-## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/48/)
+## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/48/)
## Assignment: [Train a Mountain Car](assignment.md)
diff --git a/8-Reinforcement/2-Gym/translations/README.it.md b/8-Reinforcement/2-Gym/translations/README.it.md
index 07152028e2a238857cbf0cdb92d9be7219045d69..4ed87100b5e21cce11acca77cf5f645d238f7cd0 100644
--- a/8-Reinforcement/2-Gym/translations/README.it.md
+++ b/8-Reinforcement/2-Gym/translations/README.it.md
@@ -2,7 +2,7 @@
Il problema risolto nella lezione precedente potrebbe sembrare un problema giocattolo, non propriamente applicabile a scenari di vita reale. Questo non è il caso, perché anche molti problemi del mondo reale condividono questo scenario, incluso Scacchi o Go. Sono simili, perché anche in quei casi si ha una tavolo di gioco con regole date e uno **stato discreto**.
-## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/47/?loc=it)
+## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/47/?loc=it)
## Introduzione
@@ -329,7 +329,7 @@ Si dovrebbe vedere qualcosa del genere:
> **Compito 4**: Qui non si stava selezionando l'azione migliore per ogni passaggio, ma piuttosto campionando con la corrispondente distribuzione di probabilità. Avrebbe più senso selezionare sempre l'azione migliore, con il valore Q-Table più alto? Questo può essere fatto usando la funzione `np.argmax` per trovare il numero dell'azione corrispondente al valore della Q-Table più alto. Implementare questa strategia e vedere se migliora il bilanciamento.
-## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/48/?loc=it)
+## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/48/?loc=it)
## Compito: [addestrare un'auto di montagna](assignment.it.md)
diff --git a/8-Reinforcement/2-Gym/translations/README.ko.md b/8-Reinforcement/2-Gym/translations/README.ko.md
index bf2e3256b8d2c99f1a36e25f0bbdf8f890f13b05..0002b2be0a21508fe9de53909f08849855e443ce 100644
--- a/8-Reinforcement/2-Gym/translations/README.ko.md
+++ b/8-Reinforcement/2-Gym/translations/README.ko.md
@@ -2,7 +2,7 @@
이전 강의에서 풀었던 문제는 장난감 문제처럼 보일 수 있고, 실제 시나리오에서 진짜 적용되지 않습니다. 체스나 바둑을 즐기는 것을 포함한 - 시나리오에 많은 실제 문제와 공유하기 때문에, 이 케이스는 아닙니다. 주어진 룰과 **discrete state**를 보드가 가지고 있기 때문에 비슷합니다.
-## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/47/)
+## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/47/)
## 소개
@@ -329,7 +329,7 @@ env.close()
> **Task 4**: 여기에는 각 단계에서 최상의 액션을 선택하지 않고, 일치하는 확률 분포로 샘플링했습니다. 가장 높은 Q-Table 값으로, 항상 최상의 액션을 선택하면 더 합리적인가요? `np.argmax` 함수로 높은 Q-Table 값에 해당되는 액션 숫자를 찾아서 마무리할 수 있습니다. 이 전략을 구현하고 밸런스를 개선했는지 봅니다.
-## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/48/)
+## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/48/)
## 과제: [Train a Mountain Car](../assignment.md)
diff --git a/8-Reinforcement/2-Gym/translations/README.zh-cn.md b/8-Reinforcement/2-Gym/translations/README.zh-cn.md
index 194922352ba34b393bf62d6f4e501fea5d49afed..783c809fee11c98d5b06a6f54a857589f40c0dfa 100644
--- a/8-Reinforcement/2-Gym/translations/README.zh-cn.md
+++ b/8-Reinforcement/2-Gym/translations/README.zh-cn.md
@@ -3,7 +3,7 @@
我们在上一课中一直在解决的问题可能看起来像一个玩具问题，并不真正适用于现实生活场景。事实并非如此，因为许多现实世界的问题也有这种情况——包括下国际象棋或围棋。它们很相似，因为我们也有一个具有给定规则和**离散状态**的板。
https://white-water-09ec41f0f.azurestaticapps.net/
-## [课前测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/47/)
+## [课前测验](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/47/)
## 介绍
@@ -330,7 +330,7 @@ env.close()
> **任务 4**：这里我们不是在每一步选择最佳动作，而是用相应的概率分布进行采样。始终选择具有最高 Q-Table 值的最佳动作是否更有意义？这可以通过使用 `np.argmax` 函数找出对应于较高 Q-Table 值的动作编号来完成。实施这个策略，看看它是否能改善平衡。
-## [课后测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/48/)
+## [课后测验](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/48/)
## 作业：[训练山地车](assignment.zh-cn.md)
diff --git a/9-Real-World/1-Applications/README.md b/9-Real-World/1-Applications/README.md
index e2eea0e6f09c282369b14d2087b92db5a34eb21b..7edc6aed06770afb882ba9d86581bb1f25c65fc6 100644
--- a/9-Real-World/1-Applications/README.md
+++ b/9-Real-World/1-Applications/README.md
@@ -8,7 +8,7 @@ In this curriculum, you have learned many ways to prepare data for training and
While a lot of interest in industry has been garnered by AI, which usually leverages deep learning, there are still valuable applications for classical machine learning models. You might even use some of these applications today! In this lesson, you'll explore how eight different industries and subject-matter domains use these types of models to make their applications more performant, reliable, intelligent, and valuable to users.
-## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/49/)
+## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/49/)
## 💰 Finance
@@ -152,7 +152,7 @@ https://ai.inqline.com/machine-learning-for-marketing-customer-segmentation/
Identify another sector that benefits from some of the techniques you learned in this curriculum, and discover how it uses ML.
-## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/50/)
+## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/50/)
## Review & Self Study
diff --git a/9-Real-World/1-Applications/translations/README.it.md b/9-Real-World/1-Applications/translations/README.it.md
index edf8447380b44543435f9f88a48bed32d4d42dba..180460dc44f7981a9c9f744c2f5fac1dfc47e993 100644
--- a/9-Real-World/1-Applications/translations/README.it.md
+++ b/9-Real-World/1-Applications/translations/README.it.md
@@ -7,7 +7,7 @@ In questo programma di studi si sono appresi molti modi per preparare i dati per
Sebbene l'intelligenza artificiale abbia suscitato molto interesse nell'industria, che di solito sfrutta il deep learning, esistono ancora preziose applicazioni per i modelli classici di machine learning. Si potrebbero anche usare alcune di queste applicazioni oggi! In questa lezione, si esplorerà come otto diversi settori e campi relativi all'argomento utilizzano questi tipi di modelli per rendere le loro applicazioni più performanti, affidabili, intelligenti e preziose per gli utenti.
-## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/49/?loc=it)
+## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/49/?loc=it)
## Finanza
@@ -151,7 +151,7 @@ https://ai.inqline.com/machine-learning-for-marketing-customer-segmentation/
Identificare un altro settore che beneficia di alcune delle tecniche apprese in questo programma di studi e scoprire come utilizza il machine learning.
-## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/50/?loc=it)
+## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/50/?loc=it)
## Revisione e Auto Apprendimento
diff --git a/9-Real-World/1-Applications/translations/README.ko.md b/9-Real-World/1-Applications/translations/README.ko.md
index 265b8421f1c050c481c6285acaba9bdfe2d693d5..6c1a36a43f4ea9a2b19b5318d4bab3385749ef3d 100644
--- a/9-Real-World/1-Applications/translations/README.ko.md
+++ b/9-Real-World/1-Applications/translations/README.ko.md
@@ -8,7 +8,7 @@
보통 딥러닝을 활용하는, AI로 산업에 많은 관심이 모이지만, 여전히 classical 머신러닝 모델의 가치있는 애플리케이션도 존재합니다. 오늘 이 애플리케이션 일부를 사용할 수도 있습니다! 이 강의에서, 8개 다양한 산업과 subject-matter 도메인에서 이 모델 타입으로 애플리케이션의 성능, 신뢰, 지능과, 사용자 가치를 어떻게 더 높일지 탐색할 예정입니다.
-## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/49/)
+## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/49/)
## 💰 금융
@@ -152,7 +152,7 @@ https://ai.inqline.com/machine-learning-for-marketing-customer-segmentation/
이 커리큘럼에서 배웠던 일부 기술로 이익을 낼 다른 색터를 식별하고, ML을 어떻게 사용하는지 탐색합니다.
-## [강의 후 학습](https://white-water-09ec41f0f.azurestaticapps.net/quiz/50/)
+## [강의 후 학습](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/50/)
## 검토 & 자기주도 학습
diff --git a/TRANSLATIONS.md b/TRANSLATIONS.md
index d8581817d375ec898e15d424ec197eec60ae2ccf..5e58778e99dbf2463e991b0dfa8db7a0319d5554 100644
--- a/TRANSLATIONS.md
+++ b/TRANSLATIONS.md
@@ -27,7 +27,7 @@ Similar to Readme's, please translate the assignments as well.
3. Edit the quiz-app's [translations index.js file](https://github.com/microsoft/ML-For-Beginners/blob/main/quiz-app/src/assets/translations/index.js) to add your language.
-4. Finally, edit ALL the quiz links in your translated README.md files to point directly to your translated quiz: https://white-water-09ec41f0f.azurestaticapps.net/quiz/1 becomes https://white-water-09ec41f0f.azurestaticapps.net/quiz/1?loc=id
+4. Finally, edit ALL the quiz links in your translated README.md files to point directly to your translated quiz: https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/1 becomes https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/1?loc=id
**THANK YOU**