diff --git a/1-Introduction/1-intro-to-ML/README.md b/1-Introduction/1-intro-to-ML/README.md
index db1c72b11456805ab6c0a11e835afb077122ec0f..4e73a48249c98617fe58d45305f3c46eb7b0c8ac 100644
--- a/1-Introduction/1-intro-to-ML/README.md
+++ b/1-Introduction/1-intro-to-ML/README.md
@@ -4,7 +4,7 @@
> 🎥 Click the image above for a video discussing the difference between machine learning, AI, and deep learning.
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1/)
### Introduction
@@ -96,7 +96,7 @@ 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/2/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2/)
## Review & Self Study
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 d8915f581cf7e04a16f755d2f70dfc5cd3ea528f..90798160697f94de2e57bfdf0aad7c11a351fea0 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/1?loc=fr)
+## [Quiz préalable](https://white-water-09ec41f0f.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://jolly-sea-0a877260f.azurestaticapps.net/quiz/2?loc=fr)
+## [Quiz de validation des connaissances](https://white-water-09ec41f0f.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 d0daadd89f96021ba505472b9200d3874af411e0..69a9157b598bcc97d96a66caf3f805b872d80eed 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/1/)
+## [Quiz Pra-Pelajaran](https://white-water-09ec41f0f.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://jolly-sea-0a877260f.azurestaticapps.net/quiz/2/)
+## [Quiz Pasca-Pelajaran](https://white-water-09ec41f0f.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 7d9fe43aed9f045508bfd941266bdead40cdce13..715a9c90f5ca5ef5519c48341461bd9c257e060c 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/1/)
+## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1/)
### 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/2/)
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2/)
## 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 fd3c11d1e43d53b5b844e652c9d852ad8482eec9..b88738d03e2b7908fd8275f435fefd7ac140d621 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/1?loc=ja)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.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://jolly-sea-0a877260f.azurestaticapps.net/quiz/2?loc=ja)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2?loc=ja)
## 振り返りと自習
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 cfa7e1274a8c1b77ca7476434859415a298fce16..669e649de9301700020b8dcec54910854d01b161 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/1?loc=tr)
+## [Ders öncesi sınav](https://white-water-09ec41f0f.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://jolly-sea-0a877260f.azurestaticapps.net/quiz/2?loc=tr)
+## [Ders sonrası test](https://white-water-09ec41f0f.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 45ec79be09c9873f1a2822e5494a5a1d66699874..3e8ae621e9f4760deb99e5a794f7c6d4efbeecb4 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/1/)
+## [课前测验](https://white-water-09ec41f0f.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://jolly-sea-0a877260f.azurestaticapps.net/quiz/2/)
+## [阅读后测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2/)
## 复习与自学
diff --git a/1-Introduction/2-history-of-ML/README.md b/1-Introduction/2-history-of-ML/README.md
index 67c93dbf5f5616bb59177c854217d56ca9dbe8d0..a44703d680d0ed6661902db83ccccd547e74616e 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/3/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3/)
In this lesson, we will walk through the major milestones in the history of machine learning and artificial intelligence.
@@ -101,7 +101,7 @@ 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/4/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.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 c5154176b7e5f3184b008ad0cd1a4f4f65c9b6b1..28402267ae05261fb25a5bfa0a6fe9935fc7c778 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/3/)
+## [Cuestionario previo a la conferencia](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3/)
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 se creó ningún descubrimiento científico en un vacío cultural. ¿Qué descubres?
-## [Cuestionario posterior a la conferencia](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4/)
+## [Cuestionario posterior a la conferencia](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4/)
## 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 9c59eb6fbe11bbe68e1f069f9a871aa60c662d20..efe268777f678ad8042774c0f12b3007c2726d8b 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/3?loc=fr)
+## [Quizz préalable](https://white-water-09ec41f0f.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://jolly-sea-0a877260f.azurestaticapps.net/quiz/4?loc=fr)
+## [Quiz de validation des connaissances](https://white-water-09ec41f0f.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 5e6a6f0f1f5072ca864929b3d69a6f2cdc597af0..9e695a8a9a976b44197b557d19b22423e783ba89 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/3/)
+## [Quiz Pra-Pelajaran](https://white-water-09ec41f0f.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://jolly-sea-0a877260f.azurestaticapps.net/quiz/4/)
+## [Quiz Pasca-Pelajaran](https://white-water-09ec41f0f.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 c7e59fd77e05cedfb0e355c5ae046a7c300dab24..c7d5ce234562963ce483c31dfe15e05fce3be488 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/3/)
+## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3/)
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://jolly-sea-0a877260f.azurestaticapps.net/quiz/4/)
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4/)
## 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 5c17650c7824db22c1e1373c9feb3fa7b6269f51..6ba32096aa5b124a0a3d565936a6625f131f3c2d 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/3?loc=ja)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.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://jolly-sea-0a877260f.azurestaticapps.net/quiz/4?loc=ja)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4?loc=ja)
## 振り返りと自習
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 a67f45ec4ad9363d51e05fa6bea7f6ee0ee0d1d4..af2346fb7e95579ecc1d2fe3a11e302bda44c05a 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/3?loc=tr)
+## [Ders öncesi test](https://white-water-09ec41f0f.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://jolly-sea-0a877260f.azurestaticapps.net/quiz/4?loc=tr)
+## [Ders sonrası test](https://white-water-09ec41f0f.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 8ca7e690f74d84ee8fdd2c7b13411519ed457d4f..70e85f8201be5d7380b09ecdf51e7ade3dc51358 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/3/)
+## [课前测验](https://white-water-09ec41f0f.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](https
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://jolly-sea-0a877260f.azurestaticapps.net/quiz/4/)
+## [课后测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4/)
## 复习与自学
diff --git a/1-Introduction/3-fairness/README.md b/1-Introduction/3-fairness/README.md
index 79bf4819295b011fca8ec33bb2cf097a12495690..7e9c8f6d8fa1d0241583056c587246162b9fae5f 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/5/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.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://jolly-sea-0a877260f.azurestaticapps.net/quiz/6/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.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.id.md b/1-Introduction/3-fairness/translations/README.id.md
index 6f09a148961d69643d531fa433a14a8636551a13..980cbd88d2716bacffee4678761cbe5014c9e27f 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/5/)
+## [Quiz Pra-Pelajaran](https://white-water-09ec41f0f.azurestaticapps.net/quiz/5/)
## Pengantar
@@ -90,11 +90,11 @@ Lima jenis bahaya utama ini tidak saling eksklusif, dan satu sistem dapat menunj
✅ **Diskusi**: Tinjau kembali beberapa contoh dan lihat apakah mereka menunjukkan bahaya yang berbeda.
-| | Alokasi | Kualitas Layanan | Stereotip | Fitnah | Representasi yang kurang atau berlebihan |
-| ----------------------- | :--------: | :----------------: | :----------: | :---------: | :----------------------------: |
-| Sistem perekrutan otomatis | x | x | x | | x |
-| Terjemahan mesin | | | | | |
-| Melabeli foto | | | | | |
+| | Alokasi | Kualitas Layanan | Stereotip | Fitnah | Representasi yang kurang atau berlebihan |
+| -------------------------- | :-----: | :--------------: | :-------: | :----: | :--------------------------------------: |
+| Sistem perekrutan otomatis | x | x | x | | x |
+| Terjemahan mesin | | | | | |
+| Melabeli foto | | | | | |
## Mendeteksi Ketidakadilan
@@ -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://jolly-sea-0a877260f.azurestaticapps.net/quiz/6/)
+## [Quiz Pasca-Pelajaran](https://white-water-09ec41f0f.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 3c167f096f38afc5389736a8ecdd30f2edd7da0f..e55fd6e7f9b6a2aff0b86bb387ec2b1d6315257f 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/5/)
+## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/5/)
## Introduzione
@@ -89,11 +89,11 @@ Questi cinque principali tipi di danno non si escludono a vicenda e un singolo s
✅ **Discussione**: rivisitare alcuni degli esempi e vedere se mostrano danni diversi.
-| | Allocatione | Qualita di servizio | Stereotipo | Denigrazione | Sovra o sotto rappresentazione |
-| ----------------------- | :--------: | :----------------: | :----------: | :---------: | :----------------------------: |
-| Sistema di assunzione automatizzato | x | x | x | | x |
-| Traduzione automatica | | | | | |
-| Eitchettatura foto | | | | | |
+| | Allocatione | Qualita di servizio | Stereotipo | Denigrazione | Sovra o sotto rappresentazione |
+| ----------------------------------- | :---------: | :-----------------: | :--------: | :----------: | :----------------------------: |
+| Sistema di assunzione automatizzato | x | x | x | | x |
+| Traduzione automatica | | | | | |
+| Eitchettatura foto | | | | | |
## Rilevare l'ingiustizia
@@ -137,11 +137,11 @@ Si sono identificati i danni e un gruppo interessato, in questo caso, delineato
✅ In una futura lezione sul Clustering, si vedrà come costruire questa 'matrice di confusione' nel codice
-| | percentuale di falsi positivi | Percentuale di falsi negativi |conteggio |
-| ---------- | ------------------- | ------------------- | ----- |
-| Donna | 0,37 | 0,27 | 54032 |
-| Uomo | 0,31 | 0.35 | 28620 |
-| Non binario | 0,33 | 0,31 | 1266 |
+| | percentuale di falsi positivi | Percentuale di falsi negativi | conteggio |
+| ----------- | ----------------------------- | ----------------------------- | --------- |
+| Donna | 0,37 | 0,27 | 54032 |
+| Uomo | 0,31 | 0.35 | 28620 |
+| Non binario | 0,33 | 0,31 | 1266 |
Questa tabella ci dice diverse cose. Innanzitutto, si nota che ci sono relativamente poche persone non binarie nei dati. I dati sono distorti, quindi si deve fare attenzione a come si interpretano questi numeri.
@@ -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://jolly-sea-0a877260f.azurestaticapps.net/quiz/6/)
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/6/)
## 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 9bb32639eb02b9c3f1a3ed80affc98592a0ec48d..ffa878c17192c4be70d26bbc058025947f95279c 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/5?loc=ja)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/5?loc=ja)
## イントロダクション
@@ -88,11 +88,11 @@ 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
✅ **ディスカッション**: いくつかの例を再検討し、異なる害を示しているかどうかを確認してください。
-| | アロケーション | サービスの質 | 固定観念 | 誹謗中傷 | 過剰表現/過小表現 |
-| ----------------------- | :--------: | :----------------: | :----------: | :---------: | :----------------------------: |
-| 採用システムの自動化 | x | x | x | | x |
-| 機械翻訳 | | | | | |
-| 写真のラベリング | | | | | |
+| | アロケーション | サービスの質 | 固定観念 | 誹謗中傷 | 過剰表現/過小表現 |
+| -------------------- | :------------: | :----------: | :------: | :------: | :---------------: |
+| 採用システムの自動化 | x | x | x | | x |
+| 機械翻訳 | | | | | |
+| 写真のラベリング | | | | | |
## 不公平の検出
@@ -134,11 +134,11 @@ 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
✅ 今後の"クラスタリング"のレッスンでは、この"混同行列"をコードで構築する方法をご紹介します。
-| | 偽陽性率 | 偽陰性率 | サンプル数 |
-| ---------- | ------------------- | ------------------- | ----- |
-| 女性 | 0.37 | 0.27 | 54032 |
-| 男性 | 0.31 | 0.35 | 28620 |
-| どちらにも属さない | 0.33 | 0.31 | 1266 |
+| | 偽陽性率 | 偽陰性率 | サンプル数 |
+| ------------------ | -------- | -------- | ---------- |
+| 女性 | 0.37 | 0.27 | 54032 |
+| 男性 | 0.31 | 0.35 | 28620 |
+| どちらにも属さない | 0.33 | 0.31 | 1266 |
この表から、いくつかのことがわかります。まず、データに含まれる男性と女性どちらでもない人が比較的少ないことがわかります。従ってこのデータは歪んでおり、この数字をどう解釈するかに注意が必要です。
@@ -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://jolly-sea-0a877260f.azurestaticapps.net/quiz/6?loc=ja)
+## [Post-lecture quiz](https://white-water-09ec41f0f.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.zh-cn.md b/1-Introduction/3-fairness/translations/README.zh-cn.md
index 222045441a9292756fa145a3772bc28bdf09bf98..952e81916571f4d535c37873735396a707f99c58 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/5/)
+## [课前测验](https://white-water-09ec41f0f.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://jolly-sea-0a877260f.azurestaticapps.net/quiz/6/)
+## [课后测验](https://white-water-09ec41f0f.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/4-techniques-of-ML/README.md b/1-Introduction/4-techniques-of-ML/README.md
index 70b9600021da3c1a2d435f75db4cba9dd18bad49..1b87fd7ce269868ef03dc274c6a13c88a916ab8b 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/7/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.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://jolly-sea-0a877260f.azurestaticapps.net/quiz/8/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.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 114d6e8d778c82c1683fe02db852b4b12e8ce2b0..0121527ef8d81ed38985d7af5ec302ab70dcfa2f 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/7/)
+## [Cuestionario previo a la conferencia](https://white-water-09ec41f0f.azurestaticapps.net/quiz/7/)
## 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/8/)
+## [Cuestionario posterior a la conferencia](https://white-water-09ec41f0f.azurestaticapps.net/quiz/8/)
## 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 8e6fac43ca80767b1fea58d0823458e2170e855b..699603702edc79bce3d88925529cd12d3b01ac8e 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/7/)
+## [Quiz Pra-Pelajaran](https://white-water-09ec41f0f.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://jolly-sea-0a877260f.azurestaticapps.net/quiz/8/)
+## [Quiz Pra-Pelajaran](https://white-water-09ec41f0f.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 4e36c9cc546a8eb267908798d1956704766f757e..ecfb35e5749221812664daf3346fc35aa05bc92d 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/7/)
+## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/7/)
## 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/8/)
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/8/)
## 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 b964781de24ca0b8a449a416bc8836daeee95b4d..13dcdb3127c13af05387fba86244e060671078b7 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/7?loc=ja)
+## [講義前の小テスト](https://white-water-09ec41f0f.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://jolly-sea-0a877260f.azurestaticapps.net/quiz/8?loc=ja)
+## [講義後の小テスト](https://white-water-09ec41f0f.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.zh-cn.md b/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md
index c7b86c8e2a81d5fb6aae9a8319a200c73a0a60a7..2590b99a111f712f0a3d4677ae214c01efb54a64 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/7/)
+## [课前测验](https://white-water-09ec41f0f.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://jolly-sea-0a877260f.azurestaticapps.net/quiz/8/)
+## [阅读后测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/8/)
## 复习与自学
diff --git a/2-Regression/1-Tools/README.md b/2-Regression/1-Tools/README.md
index 69986fba29c401e668c8fba84ff35c53aa0787ac..275e79f5670d5f7ef85c50decc08b7c460b8da40 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/9/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/9/)
> ### [This lesson is available in R!](./solution/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://jolly-sea-0a877260f.azurestaticapps.net/quiz/10/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.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 b7ab4bf8c18ee99f4bd7430079c268b653976e8c..87b2be53b43d35083b7d89d1559adb6999bec603 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/9/)
+## [Kuis Pra-ceramah](https://white-water-09ec41f0f.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://jolly-sea-0a877260f.azurestaticapps.net/quiz/10/)
+## [Kuis pasca-ceramah](https://white-water-09ec41f0f.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 48c61d3404fb235317f0e5704faceb33f099bdbd..a5e2e3842e6157e348c16c49b143afc811f445af 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/9/)
+## [Qui Pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/9/)
## 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/10/)
+## [Qui post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/10/)
## 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 0ecc0e8290671aae2cacb45b872749f32c029c10..fcb5ce0f7ad2465b58a4a7aeda7d876a3bdd07e2 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/9?loc=ja)
+## [講義前クイズ](https://white-water-09ec41f0f.azurestaticapps.net/quiz/9?loc=ja)
## イントロダクション
@@ -205,7 +205,7 @@ s1 tc: T細胞（白血球の一種）
## 🚀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://jolly-sea-0a877260f.azurestaticapps.net/quiz/10?loc=ja)
+## [講義後クイズ](https://white-water-09ec41f0f.azurestaticapps.net/quiz/10?loc=ja)
## レビュー & 自主学習
diff --git a/2-Regression/1-Tools/translations/README.zh-cn.md b/2-Regression/1-Tools/translations/README.zh-cn.md
index bb7f37c11f86e80a371d3597e08ab329a28c0f85..019eb06f6027ee1a6959f707682543967e1702a2 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/9/)
+## [课前测](https://white-water-09ec41f0f.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！
@@ -192,7 +192,7 @@ s1 tc：T细胞（一种白细胞）
## 🚀挑战
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://jolly-sea-0a877260f.azurestaticapps.net/quiz/10/)
+## [课后测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/10/)
## 复习与自学
diff --git a/2-Regression/2-Data/README.md b/2-Regression/2-Data/README.md
index 56493188bcf02130ef933573d0ed92ff4ae48a7a..a647eb6b4235a0646d029c272108320e6bf85c70 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/11/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/11/)
> ### [This lesson is available in R!](./solution/lesson_2-R.ipynb)
@@ -192,7 +192,7 @@ 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/12/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/12/)
## Review & Self Study
diff --git a/2-Regression/2-Data/translations/README.id.md b/2-Regression/2-Data/translations/README.id.md
index b9889ae8fbff0e831ae0c1859a9fd3287eff2962..9d8b5f18dadbe89ae1a6ad558f8a9e00c328c1d9 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/11/)
+## [Kuis pra-ceramah](https://white-water-09ec41f0f.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://jolly-sea-0a877260f.azurestaticapps.net/quiz/12/)
+## [Kuis pasca-ceramah](https://white-water-09ec41f0f.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 7b78ac52bb10249362fe02c693d72750478c7050..ba57ee11b3db55ec763f3127f1ef13f0b5837cfe 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/11/)
+## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/11/)
## 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/12/)
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/12/)
## 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 8a9f4d55220b787220de98521aff95e33bc6e972..ddd01a775e58cdedf8b98846b72e0dc3d3bc8282 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/11?loc=ja)
+## [講義前のクイズ](https://white-water-09ec41f0f.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://jolly-sea-0a877260f.azurestaticapps.net/quiz/12?loc=ja)
+## [講義後クイズ](https://white-water-09ec41f0f.azurestaticapps.net/quiz/12?loc=ja)
## レビュー & 自主学習
diff --git a/2-Regression/2-Data/translations/README.zh-cn.md b/2-Regression/2-Data/translations/README.zh-cn.md
index bc273ab1e33bc72b3bc253398ebb7381875c60ae..df542b15bf741ba683886f4a41f5d1103461c1a6 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/11/)
+## [课前测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/11/)
## 介绍
@@ -191,7 +191,7 @@
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://jolly-sea-0a877260f.azurestaticapps.net/quiz/12/)
+## [课后测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/12/)
## 复习与自学
diff --git a/2-Regression/3-Linear/README.md b/2-Regression/3-Linear/README.md
index 2a60658b2e65a84e0f8cd46a9218c2fe3369ed99..0a4e09b49794550cc35a7dcda5888d7d4a7fd63b 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/13/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/13/)
### Introduction
So far you have explored what regression is with sample data gathered from the pumpkin pricing dataset that we will use throughout this lesson. You have also visualized it using Matplotlib.
@@ -320,7 +320,7 @@ It does make sense, given the plot! And, if this is a better model than the prev
Test several different variables in this notebook to see how correlation corresponds to model accuracy.
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/14/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14/)
## Review & Self Study
diff --git a/2-Regression/3-Linear/solution/lesson_3-R.ipynb b/2-Regression/3-Linear/solution/lesson_3-R.ipynb
index 0cc040bbdf7c86e1b6441ae1414713e01cbf7ff5..09fadc992681793af9d4e470377fc9b77497ddfe 100644
--- a/2-Regression/3-Linear/solution/lesson_3-R.ipynb
+++ b/2-Regression/3-Linear/solution/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://jolly-sea-0a877260f.azurestaticapps.net/quiz/14/)\n",
+ "## [**Post-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14/)\n",
"\n",
"## **Review & Self Study**\n",
"\n",
diff --git a/2-Regression/3-Linear/solution/lesson_3.Rmd b/2-Regression/3-Linear/solution/lesson_3.Rmd
index a1b81780d50e77a657db3413d91af66af1e94bc0..91968a0656bb49b83b3a31c16fd42e0488fa3ae4 100644
--- a/2-Regression/3-Linear/solution/lesson_3.Rmd
+++ b/2-Regression/3-Linear/solution/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://jolly-sea-0a877260f.azurestaticapps.net/quiz/14/)
+## [**Post-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14/)
## **Review & Self Study**
diff --git a/2-Regression/3-Linear/translations/README.id.md b/2-Regression/3-Linear/translations/README.id.md
index f2ae6b7cfd78c803fe570d270d682b38a672cd68..ce2ee4098aaeddc9a1f2db39b33668246aa4f348 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/13/)
+## [Kuis pra-ceramah](https://white-water-09ec41f0f.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://jolly-sea-0a877260f.azurestaticapps.net/quiz/14/)
+## [Kuis pasca-ceramah](https://white-water-09ec41f0f.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 1aafa6014af66eb529c7b5790349f45de5a32bcc..81fd5a11675fe64cc2b283efa8c39e712ab77a4e 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/13/)
+## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/13/)
### 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/14/)
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14/)
## 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 cc3c3b7b074143556ee2b06a83509a910a02d9be..2dbc0f3219e5cbcd5843a4f24fc835477ca30384 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/13/)
+## [講義前のクイズ](https://white-water-09ec41f0f.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://jolly-sea-0a877260f.azurestaticapps.net/quiz/14/)
+## [講義後クイズ](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14/)
## レビュー & 自主学習
diff --git a/2-Regression/3-Linear/translations/README.zh-cn.md b/2-Regression/3-Linear/translations/README.zh-cn.md
index 55c4d293955eb1188c4f88bf9bef9e49a29855f0..12b57103e7506e3b9359bb5cd9bbaefce2e0c74a 100644
--- a/2-Regression/3-Linear/translations/README.zh-cn.md
+++ b/2-Regression/3-Linear/translations/README.zh-cn.md
@@ -2,7 +2,7 @@
> 作者[Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [课前测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/13/)
+## [课前测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/13/)
### 介绍
到目前为止，你已经通过从我们将在本课程中使用的南瓜定价数据集收集的样本数据探索了什么是回归。你还使用Matplotlib对其进行了可视化。
@@ -321,7 +321,7 @@ Scikit-learn包含一个用于构建多项式回归模型的有用API - `make_pi
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://jolly-sea-0a877260f.azurestaticapps.net/quiz/14/)
+## [课后测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14/)
## 复习与自学
diff --git a/2-Regression/4-Logistic/README.md b/2-Regression/4-Logistic/README.md
index a2a938a718ef6ec4ec6e86b1169a2fbab40bfd72..afc9c629f1a8754ef94e73672d5a5c5a3b95156f 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/15/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/)
## Introduction
@@ -222,10 +222,10 @@ While you can get a scoreboard report [terms](https://scikit-learn.org/stable/mo
In Scikit-learn, confusion matrices Rows (axis 0) are actual labels and columns (axis 1) are predicted labels.
-| |0|1|
-|:-:|:-:|:-:|
-|0|TN|FP|
-|1|FN|TP|
+| | 0 | 1 |
+| :---: | :---: | :---: |
+| 0 | TN | FP |
+| 1 | FN | TP |
What's going on here? Let's say our model is asked to classify pumpkins between two binary categories, category 'orange' and category 'not-orange'.
@@ -296,7 +296,7 @@ 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/16/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/16/)
## Review & Self Study
diff --git a/2-Regression/4-Logistic/solution/lesson_4-R.ipynb b/2-Regression/4-Logistic/solution/lesson_4-R.ipynb
index c9e16ce9ec75ca705a650619e9c37c0084369c1d..574cec05923334e629841b42c55ac115d465f515 100644
--- a/2-Regression/4-Logistic/solution/lesson_4-R.ipynb
+++ b/2-Regression/4-Logistic/solution/lesson_4-R.ipynb
@@ -45,7 +45,7 @@
{
"cell_type": "markdown",
"source": [
- "#### ** [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/15/)**\n",
+ "#### ** [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/)**\n",
"\n",
"#### Introduction\n",
"\n",
diff --git a/2-Regression/4-Logistic/solution/lesson_4.Rmd b/2-Regression/4-Logistic/solution/lesson_4.Rmd
index 8d99dc6207f06c4202761edce6eb2b49c4d991ec..e974594d1d50e436858a1aa1346de2882a0e5850 100644
--- a/2-Regression/4-Logistic/solution/lesson_4.Rmd
+++ b/2-Regression/4-Logistic/solution/lesson_4.Rmd
@@ -14,7 +14,7 @@ output:
{width="600"}
-#### ** [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/15/)**
+#### ** [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/)**
#### Introduction
diff --git a/2-Regression/4-Logistic/translations/README.id.md b/2-Regression/4-Logistic/translations/README.id.md
index 213ef4aa3dfd0324b3b498f5954371c46bfd165d..553205d71288b73b1423c26d09da7e8f7b078826 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/15/)
+## [Kuis pra-ceramah](https://white-water-09ec41f0f.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://jolly-sea-0a877260f.azurestaticapps.net/quiz/16/)
+## [Kuis pasca-ceramah](https://white-water-09ec41f0f.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 e9940203ce0f4a62368fc2dc22c77bcdd12bfeb8..deb51eaa1e62de6a4e7ce9f956b4734714f2137c 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/15/)
+## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/)
## 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/16/)
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/16/)
## 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 3e384824b23fe3db11baba8d997815395e3c839c..662a1eafba82c8c58b761c3fbed78a5eb7056a47 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/15/)
+## [講義前のクイズ](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/)
## イントロダクション
@@ -225,10 +225,10 @@ Seabornプロットでは、変数を並べて表示することができます
Scikit-learnでは、混同行列の行 (axis=0)が実際のラベル、列 (axis=1)が予測ラベルとなります。
-| |0|1|
-|:-:|:-:|:-:|
-|0|TN|FP|
-|1|FN|TP|
+| | 0 | 1 |
+| :---: | :---: | :---: |
+| 0 | TN | FP |
+| 1 | FN | TP |
ここで何が起こっているのか？例えば、カボチャを「オレンジ色」と「オレンジ色でない」という2つのカテゴリーに分類するように求められたとしましょう。
@@ -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://jolly-sea-0a877260f.azurestaticapps.net/quiz/16/)
+## [講義後クイズ](https://white-water-09ec41f0f.azurestaticapps.net/quiz/16/)
## レビュー & 自主学習
diff --git a/2-Regression/4-Logistic/translations/README.zh-cn.md b/2-Regression/4-Logistic/translations/README.zh-cn.md
index 5de169fbec81517414676d4441166e5a13d89780..dc1bb5e10b4282ccbff4685001b89fb075a68bb0 100644
--- a/2-Regression/4-Logistic/translations/README.zh-cn.md
+++ b/2-Regression/4-Logistic/translations/README.zh-cn.md
@@ -2,7 +2,7 @@
> 作者[Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [课前测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/15/)
+## [课前测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/)
## 介绍
@@ -282,7 +282,7 @@ 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/16/)
+## [课后测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/16/)
## 复习与自学
diff --git a/3-Web-App/1-Web-App/README.md b/3-Web-App/1-Web-App/README.md
index fae04026d530fc53d2d307dc43faadf62e44fc25..c983e679a924673201afdae716bb0da4a4304d5e 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/17/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.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://jolly-sea-0a877260f.azurestaticapps.net/quiz/18/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/18/)
## Review & Self Study
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 82454852a2d055f4872f0de0d1c9f38005518d19..f31eb2d4b508f5f4e73873982de4ae83b0dc6bb0 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/17/)
+## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/17/)
## 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/18/)
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/18/)
## 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 edfaa34260c2353494ed2385c49eb0bd47e6e052..b23050dd62f6255ec47242b87787872321ce575a 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/17?loc=ja)
+## [講義前の小テスト](https://white-water-09ec41f0f.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://jolly-sea-0a877260f.azurestaticapps.net/quiz/18?loc=ja)
+## [講義後の小テスト](https://white-water-09ec41f0f.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.zh-cn.md b/3-Web-App/1-Web-App/translations/README.zh-cn.md
index 9110f2b864c13f6dbb751d327b239579cd616124..e53b11bcd54236c010e001edcabb4c3c62fbf0d4 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/17/)
+## [课前测](https://white-water-09ec41f0f.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://jolly-sea-0a877260f.azurestaticapps.net/quiz/18/)
+## [课后测](https://white-water-09ec41f0f.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 4a3373767b0199a80bd2119f0e565f2be59baed6..b6dde02f7a6cbc1786e1d3d0f25552f1e2f5be77 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/19/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/19/)
### Introduction
@@ -286,7 +286,7 @@ 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/20/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/20/)
## Review & Self Study
diff --git a/4-Classification/1-Introduction/translations/README.it.md b/4-Classification/1-Introduction/translations/README.it.md
index fabfec5e25ce8799ad87a4eabcb91242cac6a998..76c8b0a2797d4e87982ec8d80d71df362390a80f 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/19/)
+## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/19/)
### 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/20/)
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/20/)
## Revisione e Auto Apprendimento
diff --git a/4-Classification/1-Introduction/translations/README.tr.md b/4-Classification/1-Introduction/translations/README.tr.md
index a2b1d92f3213d89177ff84539436f0f169babcdf..e1b32ec90be1c8f1df0a0fc1faab6173112546a1 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/19/?loc=tr)
+## [Ders öncesi kısa sınavı](https://white-water-09ec41f0f.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://jolly-sea-0a877260f.azurestaticapps.net/quiz/20/?loc=tr)
+## [Ders sonrası kısa sınavı](https://white-water-09ec41f0f.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 df585e3f5b19375dcd2009636c83bf215d1b3a14..978f4a0674276009386388042dbb0c2432cf0c08 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/19/)
+## [课程前的小问题](https://white-water-09ec41f0f.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://jolly-sea-0a877260f.azurestaticapps.net/quiz/20/)
+## [课后练习](https://white-water-09ec41f0f.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 c6b35219f973bfc8330b74f817aeb0e930cc2397..68877e6ace31a23e7c880bc511bce58378282a26 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/21/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.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.
@@ -67,13 +67,13 @@ Assuming you completed [Lesson 1](../1-Introduction/README.md), make sure that a
Your features look like this:
-| | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | artemisia | artichoke | ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood | yam | yeast | yogurt | zucchini |
-| -----: | -------: | ----: | ---------: | ----: | -----------: | ------: | -------: | --------: | --------: | ---: | ------: | ----------: | ---------: | ----------------------: | ---: | ---: | ---: | ----: | -----: | -------: | -----: |
-| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
+| | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | artemisia | artichoke | ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood | yam | yeast | yogurt | zucchini |
+| ---: | -----: | -------: | ----: | ---------: | ----: | -----------: | ------: | -------: | --------: | --------: | ---: | ------: | ----------: | ---------: | ----------------------: | ---: | ---: | ---: | ----: | -----: | -------: |
+| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
+| 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
+| 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
+| 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
+| 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
Now you are ready to train your model!
@@ -199,8 +199,8 @@ Since you are using the multiclass case, you need to choose what _scheme_ to use
The result is printed - Indian cuisine is its best guess, with good probability:
- | | 0 |
- | -------: | -------: |
+ | | 0 |
+ | -------: | -------: |
| indian | 0.715851 |
| chinese | 0.229475 |
| japanese | 0.029763 |
@@ -217,21 +217,21 @@ Since you are using the multiclass case, you need to choose what _scheme_ to use
```
| | precision | recall | f1-score | support |
- | ------------ | ------ | -------- | ------- | ---- |
- | chinese | 0.73 | 0.71 | 0.72 | 229 |
- | indian | 0.91 | 0.93 | 0.92 | 254 |
- | japanese | 0.70 | 0.75 | 0.72 | 220 |
- | korean | 0.86 | 0.76 | 0.81 | 242 |
- | thai | 0.79 | 0.85 | 0.82 | 254 |
- | accuracy | 0.80 | 1199 | | |
- | macro avg | 0.80 | 0.80 | 0.80 | 1199 |
- | weighted avg | 0.80 | 0.80 | 0.80 | 1199 |
+ | ------------ | --------- | ------ | -------- | ------- |
+ | chinese | 0.73 | 0.71 | 0.72 | 229 |
+ | indian | 0.91 | 0.93 | 0.92 | 254 |
+ | japanese | 0.70 | 0.75 | 0.72 | 220 |
+ | korean | 0.86 | 0.76 | 0.81 | 242 |
+ | thai | 0.79 | 0.85 | 0.82 | 254 |
+ | accuracy | 0.80 | 1199 | | |
+ | macro avg | 0.80 | 0.80 | 0.80 | 1199 |
+ | weighted avg | 0.80 | 0.80 | 0.80 | 1199 |
## 🚀Challenge
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://jolly-sea-0a877260f.azurestaticapps.net/quiz/22/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/22/)
## Review & Self Study
diff --git a/4-Classification/2-Classifiers-1/translations/README.it.md b/4-Classification/2-Classifiers-1/translations/README.it.md
index 674a59c2699901972a8b28d7a4dd29147f71915c..5611870b4e628267b56fa37bd8729d7d33934013 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/21/)
+## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/21/)
# 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.
@@ -200,13 +200,13 @@ Poiché si sta utilizzando il caso multiclasse, si deve scegliere quale _schema_
Il risultato è stampato: la cucina indiana è la sua ipotesi migliore, con buone probabilità:
- | | 0 | | | | | | | | | | | | | | | | | | | | |
- | -------: | -------: | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
- | indiano | 0,715851 | | | | | | | | | | | | | | | | | | | | |
- | cinese | 0.229475 | | | | | | | | | | | | | | | | | | | | |
+ | | 0 | | | | | | | | | | | | | | | | | | | | |
+ | ---------: | -------: | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
+ | indiano | 0,715851 | | | | | | | | | | | | | | | | | | | | |
+ | cinese | 0.229475 | | | | | | | | | | | | | | | | | | | | |
| Giapponese | 0,029763 | | | | | | | | | | | | | | | | | | | | |
- | Coreano | 0.017277 | | | | | | | | | | | | | | | | | | | | |
- | thai | 0.007634 | | | | | | | | | | | | | | | | | | | | |
+ | Coreano | 0.017277 | | | | | | | | | | | | | | | | | | | | |
+ | thai | 0.007634 | | | | | | | | | | | | | | | | | | | | |
✅ Si è in grado di spiegare perché il modello è abbastanza sicuro che questa sia una cucina indiana?
@@ -217,22 +217,22 @@ Poiché si sta utilizzando il caso multiclasse, si deve scegliere quale _schema_
print(classification_report(y_test,y_pred))
```
- | precisione | recall | punteggio f1 | supporto | | | | | | | | | | | | | | | | | | |
- | ------------ | ------ | -------- | ------- | ---- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
- | cinese | 0,73 | 0,71 | 0,72 | 229 | | | | | | | | | | | | | | | | | |
- | indiano | 0,91 | 0,93 | 0,92 | 254 | | | | | | | | | | | | | | | | | |
- | Giapponese | 0.70 | 0,75 | 0,72 | 220 | | | | | | | | | | | | | | | | | |
- | Coreano | 0,86 | 0,76 | 0,81 | 242 | | | | | | | | | | | | | | | | | |
- | thai | 0,79 | 0,85 | 0.82 | 254 | | | | | | | | | | | | | | | | | |
- | accuratezza | 0,80 | 1199 | | | | | | | | | | | | | | | | | | | |
- | macro media | 0,80 | 0,80 | 0,80 | 1199 | | | | | | | | | | | | | | | | | |
- | Media ponderata | 0,80 | 0,80 | 0,80 | 1199 | | | | | | | | | | | | | | | | | |
+ | precisione | recall | punteggio f1 | supporto | | | | | | | | | | | | | | | | | | |
+ | --------------- | ------ | ------------ | -------- | ---- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
+ | cinese | 0,73 | 0,71 | 0,72 | 229 | | | | | | | | | | | | | | | | | |
+ | indiano | 0,91 | 0,93 | 0,92 | 254 | | | | | | | | | | | | | | | | | |
+ | Giapponese | 0.70 | 0,75 | 0,72 | 220 | | | | | | | | | | | | | | | | | |
+ | Coreano | 0,86 | 0,76 | 0,81 | 242 | | | | | | | | | | | | | | | | | |
+ | thai | 0,79 | 0,85 | 0.82 | 254 | | | | | | | | | | | | | | | | | |
+ | accuratezza | 0,80 | 1199 | | | | | | | | | | | | | | | | | | | |
+ | macro media | 0,80 | 0,80 | 0,80 | 1199 | | | | | | | | | | | | | | | | | |
+ | Media ponderata | 0,80 | 0,80 | 0,80 | 1199 | | | | | | | | | | | | | | | | | |
## 🚀 Sfida
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://jolly-sea-0a877260f.azurestaticapps.net/quiz/22/)
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/22/)
## 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.tr.md b/4-Classification/2-Classifiers-1/translations/README.tr.md
index f02bd759a9cabdc1ca48b2176d04ea36da9672eb..30f36b133cc1ab69c522d505318a6c3cb5caf0c5 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/21/?loc=tr)
+## [Ders öncesi kısa sınavı](https://white-water-09ec41f0f.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.
@@ -68,12 +68,12 @@ Bu veri setini çeşitli sınıflandırıcılarla _bir grup malzemeyi baz alarak
Öznitelikleriniz şöyle görünüyor:
| almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | artemisia | artichoke | ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood | yam | yeast | yogurt | zucchini |
-| -----: | -------: | ----: | ---------: | ----: | -----------: | ------: | -------: | --------: | --------: | ---: | ------: | ----------: | ---------: | ----------------------: | ---: | ---: | ---: | ----: | -----: | -------: |
-| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
+| -----: | -------: | ----: | ---------: | ----: | -----------: | ------: | -------: | --------: | --------: | ---: | ------: | ----------: | ---------: | ----------------------: | ---: | ---: | ---: | ----: | -----: | -------: |
+| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
+| 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
+| 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
+| 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
+| 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
Şimdi modelinizi eğitmek için hazırsınız!
@@ -199,8 +199,8 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine
Sonuç bastırılır - Hint mutfağı iyi olasılıkla en iyi öngörü:
- | | 0 |
- | -------: | -------: |
+ | | 0 |
+ | -------: | -------: |
| indian | 0.715851 |
| chinese | 0.229475 |
| japanese | 0.029763 |
@@ -217,21 +217,21 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine
```
| | precision | recall | f1-score | support |
- | ------------ | ------ | -------- | ------- | ---- |
- | chinese | 0.73 | 0.71 | 0.72 | 229 |
- | indian | 0.91 | 0.93 | 0.92 | 254 |
- | japanese | 0.70 | 0.75 | 0.72 | 220 |
- | korean | 0.86 | 0.76 | 0.81 | 242 |
- | thai | 0.79 | 0.85 | 0.82 | 254 |
- | accuracy | 0.80 | 1199 | | |
- | macro avg | 0.80 | 0.80 | 0.80 | 1199 |
- | weighted avg | 0.80 | 0.80 | 0.80 | 1199 |
+ | ------------ | --------- | ------ | -------- | ------- |
+ | chinese | 0.73 | 0.71 | 0.72 | 229 |
+ | indian | 0.91 | 0.93 | 0.92 | 254 |
+ | japanese | 0.70 | 0.75 | 0.72 | 220 |
+ | korean | 0.86 | 0.76 | 0.81 | 242 |
+ | thai | 0.79 | 0.85 | 0.82 | 254 |
+ | accuracy | 0.80 | 1199 | | |
+ | macro avg | 0.80 | 0.80 | 0.80 | 1199 |
+ | weighted avg | 0.80 | 0.80 | 0.80 | 1199 |
## :rocket: Meydan Okuma
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://jolly-sea-0a877260f.azurestaticapps.net/quiz/22/?loc=tr)
+## [Ders sonrası kısa sınavı](https://white-water-09ec41f0f.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 83aa4fc4c2ee65070e2e6c878ed42e842177b0ca..479533338728c0df70cb226f722ca9251e62a038 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/21/)
+## [课前测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/21/)
# 准备工作
假如你已经完成了[课程1](../../1-Introduction/translations/README.zh-cn.md), 确保在根目录的`/data`文件夹中有 _cleaned_cuisines.csv_ 这份文件来进行接下来的四节课程。
@@ -68,13 +68,13 @@
你的特征集看上去将会是这样:
- | | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | artemisia | artichoke | ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood | yam | yeast | yogurt | zucchini |
- | -----: | -------: | ----: | ---------: | ----: | -----------: | ------: | -------: | --------: | --------: | ---: | ------: | ----------: | ---------: | ----------------------: | ---: | ---: | ---: | ----: | -----: | -------: | --- |
- | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
- | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
- | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
- | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
- | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
+ | | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | artemisia | artichoke | ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood | yam | yeast | yogurt | zucchini |
+ | ---: | -----: | -------: | ----: | ---------: | ----: | -----------: | ------: | -------: | --------: | --------: | ---: | ------: | ----------: | ---------: | ----------------------: | ---: | ---: | ---: | ----: | -----: | -------- |
+ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
+ | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
+ | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
+ | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
+ | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
现在，你已经准备好可以开始训练你的模型了！
@@ -201,11 +201,11 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine
运行后的输出如下———可以发现这是一道印度菜的可能性最大，是最合理的猜测:
| | 0 |
- | -------: | -------: |
+ | -------: | -------: |
| indian | 0.715851 |
| chinese | 0.229475 |
| japanese | 0.029763 |
- | korean | 0.017277 |
+ | korean | 0.017277 |
| thai | 0.007634 |
✅ 你能解释下为什么模型会如此确定这是一道印度菜么？
@@ -218,8 +218,8 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine
```
| precision | recall | f1-score | support | |
- | ------------ | ------ | -------- | ------- | ---- |
- | chinese | 0.73 | 0.71 | 0.72 | 229 |
+ | ------------ | ------ | -------- | ------- | ---- |
+ | chinese | 0.73 | 0.71 | 0.72 | 229 |
| indian | 0.91 | 0.93 | 0.92 | 254 |
| japanese | 0.70 | 0.75 | 0.72 | 220 |
| korean | 0.86 | 0.76 | 0.81 | 242 |
@@ -232,7 +232,7 @@ 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/22/)
+## [课后测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/22/)
## 回顾与自学
[这个课程](https://people.eecs.berkeley.edu/~russell/classes/cs194/f11/lectures/CS194%20Fall%202011%20Lecture%2006.pdf)将对逻辑回归背后的数学原理进行更加深入的讲解
diff --git a/4-Classification/3-Classifiers-2/README.md b/4-Classification/3-Classifiers-2/README.md
index 9720c76379fa9d1e25f4405510fbded52f5baacd..291b3a75151e42441f0864ca6c6749d6e5b9db72 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/23/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.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://jolly-sea-0a877260f.azurestaticapps.net/quiz/24/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/24/)
## Review & Self Study
diff --git a/4-Classification/3-Classifiers-2/translations/README.it.md b/4-Classification/3-Classifiers-2/translations/README.it.md
index 4a3a431fe33addc3e7c249a06a03b1b8bbbe3806..3d011db879d0dca151c5a71bfa19bfd4de6e0362 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/23/)
+## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/23/)
### 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/24/)
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/24/)
## Revisione e Auto Apprendimento
diff --git a/4-Classification/3-Classifiers-2/translations/README.tr.md b/4-Classification/3-Classifiers-2/translations/README.tr.md
index 24ea3dfd84a68ad9b45ea1a1a178f8865712e51f..56c1e11e9fd12afb060f092867c16d7872ae30dd 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/23/?loc=tr)
+## [Ders öncesi kısa sınavı](https://white-water-09ec41f0f.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://jolly-sea-0a877260f.azurestaticapps.net/quiz/24/?loc=tr)
+## [Ders sonrası kısa sınavı](https://white-water-09ec41f0f.azurestaticapps.net/quiz/24/?loc=tr)
## Gözden Geçirme & Kendi Kendine Çalışma
diff --git a/4-Classification/4-Applied/README.md b/4-Classification/4-Applied/README.md
index 66597fbf6da763f6b3216b7c4aadbe89eb683b14..cad728f34cf02461f0a54d626f61f60ea7cd2375 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: Andrew Ng introduces recommendation system design
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/25/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/25/)
In this lesson you will learn:
@@ -321,7 +321,7 @@ 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/26/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/26/)
## Review & Self Study
diff --git a/4-Classification/4-Applied/translations/README.it.md b/4-Classification/4-Applied/translations/README.it.md
index 4512aabacb24e4ff6c4e171786c7108e15048cce..72dc6c38007cf712d9fd8341cc34a0e24c542b76 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: Andrew Ng introduce la progettazione di un sistema di raccomandazione
-## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/25/)
+## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/25/)
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://jolly-sea-0a877260f.azurestaticapps.net/quiz/26/)
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/26/)
## Revisione e Auto Apprendimento
diff --git a/4-Classification/4-Applied/translations/README.tr.md b/4-Classification/4-Applied/translations/README.tr.md
index 651fc85b7a77b2d661339b3ba1e62fc1f3bcd8fe..d3418e9ab88735d59f5ca8ad38e3fa763ed94d27 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: Andrew Ng introduces recommendation system design (Andrew Ng önerici sistem tasarımını tanıtıyor)
-## [Ders öncesi kısa sınavı](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/25/?loc=tr)
+## [Ders öncesi kısa sınavı](https://white-water-09ec41f0f.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://jolly-sea-0a877260f.azurestaticapps.net/quiz/26/?loc=tr)
+## [Ders sonrası kısa sınavı](https://white-water-09ec41f0f.azurestaticapps.net/quiz/26/?loc=tr)
## Gözden Geçirme & Kendi Kendine Çalışma
diff --git a/5-Clustering/1-Visualize/README.md b/5-Clustering/1-Visualize/README.md
index 8453c451293eb4869f7a3ed6f34c51a5cdd1bbfb..c5ef453f5270bf95486eddef2055ace61be77cd0 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/27/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.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://jolly-sea-0a877260f.azurestaticapps.net/quiz/28/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/28/)
## Review & Self Study
diff --git a/5-Clustering/1-Visualize/translations/README.it.md b/5-Clustering/1-Visualize/translations/README.it.md
index 104507c914525d6922b8c5898cc9b9c5be1bee25..0d8510b00902fa67f17e555a3fb7c3c254c8078a 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/27/)
+## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/27/)
### Introduzione
@@ -33,18 +33,18 @@ In questo [modulo di apprendimento](https://docs.microsoft.com/learn/modules/tra
[Scikit-learn offre una vasta gamma](https://scikit-learn.org/stable/modules/clustering.html) di metodi per eseguire il clustering. Il tipo scelto dipenderà dal caso d'uso. Secondo la documentazione, ogni metodo ha diversi vantaggi. Ecco una tabella semplificata dei metodi supportati da Scikit-learn e dei loro casi d'uso appropriati:
-| Nome del metodo | Caso d'uso |
-| :--------------------------- | :--------------------------------------------------------------------- |
-| K-MEANS | uso generale, induttivo |
-| Affinity propagation (Propagazione dell'affinità) | molti, cluster irregolari, induttivo |
-| Mean-shift (Spostamento medio) | molti, cluster irregolari, induttivo |
-| Spectral clustering (Raggruppamento spettrale) | pochi, anche grappoli, trasduttivi |
-| Ward hierarchical clustering (Cluster gerarchico) | molti, cluster vincolati, trasduttivi |
-| Agglomerative clustering (Raggruppamento agglomerativo) | molte, vincolate, distanze non euclidee, trasduttive |
-| DBSCAN | geometria non piatta, cluster irregolari, trasduttivo |
-| OPTICS | geometria non piatta, cluster irregolari con densità variabile, trasduttivo |
-| Gaussian mixtures (miscele gaussiane) | geometria piana, induttiva |
-| BIRCH | insiemi di dati di grandi dimensioni con valori anomali, induttivo |
+| Nome del metodo | Caso d'uso |
+| :------------------------------------------------------ | :-------------------------------------------------------------------------- |
+| K-MEANS | uso generale, induttivo |
+| Affinity propagation (Propagazione dell'affinità) | molti, cluster irregolari, induttivo |
+| Mean-shift (Spostamento medio) | molti, cluster irregolari, induttivo |
+| Spectral clustering (Raggruppamento spettrale) | pochi, anche grappoli, trasduttivi |
+| Ward hierarchical clustering (Cluster gerarchico) | molti, cluster vincolati, trasduttivi |
+| Agglomerative clustering (Raggruppamento agglomerativo) | molte, vincolate, distanze non euclidee, trasduttive |
+| DBSCAN | geometria non piatta, cluster irregolari, trasduttivo |
+| OPTICS | geometria non piatta, cluster irregolari con densità variabile, trasduttivo |
+| Gaussian mixtures (miscele gaussiane) | geometria piana, induttiva |
+| BIRCH | insiemi di dati di grandi dimensioni con valori anomali, induttivo |
> 🎓 Il modo in cui si creno i cluster ha molto a che fare con il modo in cui si raccolgono punti dati in gruppi. Si esamina un po' di vocabolario:
>
@@ -197,16 +197,16 @@ Il clustering come tecnica è notevolmente aiutato da una corretta visualizzazio
df.describe()
```
- | | release_date | lenght | popularity | danceability | acousticness | Energia | strumentale | vitalità | livello di percezione sonora | parlato | tempo | #ora_firma |
- | ----- | ------------ | ----------- | ---------- | ------------ | ------------ | -------- | ---------------- | -------- | --------- | ----------- | ---------- | -------------- |
- | estero) | 530 | 530 | 530 | 530 | 530 | 530 | 530 | 530 | 530 | 530 | 530 | 530 |
- | mezzo | 2015.390566 | 222298.1698 | 17.507547 | 0.741619 | 0.265412 | 0.760623 | 0,016305 | 0,147308 | -4.953011 | 0,130748 | 116.487864 | 3.986792 |
- | std | 3.131688 | 39696.82226 | 18.992212 | 0,117522 | 0.208342 | 0.148533 | 0.090321 | 0,123588 | 2.464186 | 0,092939 | 23.518601 | 0.333701 |
- | min | 1998 | 89488 | 0 | 0,255 | 0,000665 | 0,111 | 0 | 0,0283 | -19,362 | 0,0278 | 61.695 | 3 |
- | 25% | 2014 | 199305 | 0 | 0,681 | 0,089525 | 0,669 | 0 | 0,07565 | -6.29875 | 0,0591 | 102.96125 | 4 |
- | 50% | 2016 | 218509 | 13 | 0,761 | 0.2205 | 0.7845 | 0.000004 | 0,1035 | -4.5585 | 0,09795 | 112.7145 | 4 |
- | 75% | 2017 | 242098.5 | 31 | 0,8295 | 0.403 | 0.87575 | 0.000234 | 0,164 | -3.331 | 0,177 | 125.03925 | 4 |
- | max | 2020 | 511738 | 73 | 0.966 | 0,954 | 0,995 | 0,91 | 0,811 | 0,582 | 0.514 | 206.007 | 5 |
+ | | release_date | lenght | popularity | danceability | acousticness | Energia | strumentale | vitalità | livello di percezione sonora | parlato | tempo | #ora_firma |
+ | ------- | ------------ | ----------- | ---------- | ------------ | ------------ | -------- | ----------- | -------- | ---------------------------- | -------- | ---------- | ---------- |
+ | estero) | 530 | 530 | 530 | 530 | 530 | 530 | 530 | 530 | 530 | 530 | 530 | 530 |
+ | mezzo | 2015.390566 | 222298.1698 | 17.507547 | 0.741619 | 0.265412 | 0.760623 | 0,016305 | 0,147308 | -4.953011 | 0,130748 | 116.487864 | 3.986792 |
+ | std | 3.131688 | 39696.82226 | 18.992212 | 0,117522 | 0.208342 | 0.148533 | 0.090321 | 0,123588 | 2.464186 | 0,092939 | 23.518601 | 0.333701 |
+ | min | 1998 | 89488 | 0 | 0,255 | 0,000665 | 0,111 | 0 | 0,0283 | -19,362 | 0,0278 | 61.695 | 3 |
+ | 25% | 2014 | 199305 | 0 | 0,681 | 0,089525 | 0,669 | 0 | 0,07565 | -6.29875 | 0,0591 | 102.96125 | 4 |
+ | 50% | 2016 | 218509 | 13 | 0,761 | 0.2205 | 0.7845 | 0.000004 | 0,1035 | -4.5585 | 0,09795 | 112.7145 | 4 |
+ | 75% | 2017 | 242098.5 | 31 | 0,8295 | 0.403 | 0.87575 | 0.000234 | 0,164 | -3.331 | 0,177 | 125.03925 | 4 |
+ | max | 2020 | 511738 | 73 | 0.966 | 0,954 | 0,995 | 0,91 | 0,811 | 0,582 | 0.514 | 206.007 | 5 |
> 🤔 Se si sta lavorando con il clustering, un metodo non supervisionato che non richiede dati etichettati, perché si stanno mostrando questi dati con etichette? Nella fase di esplorazione dei dati, sono utili, ma non sono necessari per il funzionamento degli algoritmi di clustering. Si potrebbero anche rimuovere le intestazioni delle colonne e fare riferimento ai dati per numero di colonna.
@@ -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://jolly-sea-0a877260f.azurestaticapps.net/quiz/28/)
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/28/)
## Revisione e Auto Apprendimento
diff --git a/5-Clustering/1-Visualize/translations/README.zh-cn.md b/5-Clustering/1-Visualize/translations/README.zh-cn.md
index 5f41ffa7e64e78991a3307a0c8c252677cce61bf..e3161473b20d9ce8ff3f4f0032760c13567d8219 100644
--- a/5-Clustering/1-Visualize/translations/README.zh-cn.md
+++ b/5-Clustering/1-Visualize/translations/README.zh-cn.md
@@ -4,7 +4,7 @@
[](https://youtu.be/ty2advRiWJM "No One Like You by PSquare")
> 🎥 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/27/)
+## [课前测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/27/)
### 介绍
[聚类](https://link.springer.com/referenceworkentry/10.1007%2F978-0-387-30164-8_124)对于数据探索非常有用。让我们看看它是否有助于发现尼日利亚观众消费音乐的趋势和模式。
@@ -323,7 +323,7 @@
聚类试图解决什么样的问题？
-## [课后测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/28/)
+## [课后测验](https://white-water-09ec41f0f.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 d85654ae355d8db074a9463c934d4d9f17b3aace..3f4c5fca6b1d4fd332f671838854371858ddee00 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/29/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.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://jolly-sea-0a877260f.azurestaticapps.net/quiz/30/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/30/)
## Review & Self Study
diff --git a/5-Clustering/2-K-Means/translations/README.it.md b/5-Clustering/2-K-Means/translations/README.it.md
index ce1dc9d27107655ec55d1ecd3a61764fe659e3bb..9c94d19f578c2e420d0fb947162bd106ed83a937 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/29/)
+## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/29/)
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://jolly-sea-0a877260f.azurestaticapps.net/quiz/30/)
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/30/)
## Revisione e Auto Apprendimento
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 32a3301555eb9cfd3798ec27417879c6a8665a39..a5348eacb35c5aeeab4c30ed384316deaf6677dc 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/29/)
+## [课前测验](https://white-water-09ec41f0f.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://jolly-sea-0a877260f.azurestaticapps.net/quiz/30/)
+## [课后测验](https://white-water-09ec41f0f.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 5123585624794f552f63e9abf73d97b64ebed13f..ef7444cb587f53b99d295c265e4111d67f690047 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/31/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.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://jolly-sea-0a877260f.azurestaticapps.net/quiz/32/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/32/)
## Review & Self Study
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 e85bd13611bb2f6219d172cf07dde09f2f70d9f8..67c0406e369682b50c4cbb7e5a75e045d65164f1 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/31/)
+## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/31/)
## 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/32/)
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/32/)
## Revisione e Auto Apprendimento
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 e9df88a322cbd20c4aecd3cf79be22bb47dfcb99..3826ccfd12ee39784899d13d183b9356a6c2dfd8 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/31/)
+## [课前测验](https://white-water-09ec41f0f.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。
@@ -146,7 +146,7 @@
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://jolly-sea-0a877260f.azurestaticapps.net/quiz/32/)
+## [课后测验](https://white-water-09ec41f0f.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 d816b7fe3b84806ca8d78a5bff5713ca979f317c..829df67baa7810cf4ac80db5fb9571a2cf57c22b 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/33/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.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://jolly-sea-0a877260f.azurestaticapps.net/quiz/34/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/34/)
## Review & Self Study
diff --git a/6-NLP/2-Tasks/translations/README.it.md b/6-NLP/2-Tasks/translations/README.it.md
index afe455e737156b5e90cac99e9f0b6721ce923dba..d553eea3bfd16f5fe31d3a92f89e7481a9e06ca7 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/33/)
+## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/33/)
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://jolly-sea-0a877260f.azurestaticapps.net/quiz/34/)
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/34/)
## Revisione e Auto Apprendimento
diff --git a/6-NLP/3-Translation-Sentiment/README.md b/6-NLP/3-Translation-Sentiment/README.md
index 0c6b568bf5aecc3ffff8187a24c6b2ba712adbd0..dd6bd1bffc9fceef5c19bddc03b038b9a1dc7148 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/35/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.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://jolly-sea-0a877260f.azurestaticapps.net/quiz/36/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/36/)
## Review & Self Study
diff --git a/6-NLP/3-Translation-Sentiment/translations/README.it.md b/6-NLP/3-Translation-Sentiment/translations/README.it.md
index 32e52922f81e900cabf48ad9d2c12447d7d09ae8..d1116a9e084f74487fb4da0fa83b609b59bc2639 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/35/)
+## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/35/)
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://jolly-sea-0a877260f.azurestaticapps.net/quiz/36/)
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/36/)
## Revisione e Auto Apprendimento
diff --git a/6-NLP/4-Hotel-Reviews-1/README.md b/6-NLP/4-Hotel-Reviews-1/README.md
index a4b5a556f7bc774313d134483ee0148f5589df3d..dd6a573a9e2f5d12a81ec095f0a0f20c77bf8921 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/37/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.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://jolly-sea-0a877260f.azurestaticapps.net/quiz/38/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/38/)
## Review & Self Study
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 a541d0d6f41af6afe67fbd7efe46cf0e366e1cb0..cbbca6109c01ecb45c81240fe492a626513fb4d1 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/37/)
+## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/37/)
### Introduzione
@@ -287,15 +287,15 @@ Trattare le seguenti domande come attività di codifica e provare a rispondere s
display(hotel_freq_df)
```
- | Hotel_Name (Nome Hotel) | Total_Number_of_Reviews (Numero totale di recensioni) | Total_Reviews_Found |
- | :----------------------------------------: | :---------------------: | :-----------------: |
- | Britannia International Hotel Canary Wharf | 9086 | 4789 |
- | Park Plaza Westminster Bridge Londra | 12158 | 4169 |
- | Copthorne Tara Hotel London Kensington | 7105 | 3578 |
- | ... | ... | ... |
- | Mercure Paris Porte d'Orléans | 110 | 10 |
- | Hotel Wagner | 135 | 10 |
- | Hotel Gallitzinberg | 173 | 8 |
+ | Hotel_Name (Nome Hotel) | Total_Number_of_Reviews (Numero totale di recensioni) | Total_Reviews_Found |
+ | :----------------------------------------: | :---------------------------------------------------: | :-----------------: |
+ | Britannia International Hotel Canary Wharf | 9086 | 4789 |
+ | Park Plaza Westminster Bridge Londra | 12158 | 4169 |
+ | Copthorne Tara Hotel London Kensington | 7105 | 3578 |
+ | ... | ... | ... |
+ | Mercure Paris Porte d'Orléans | 110 | 10 |
+ | Hotel Wagner | 135 | 10 |
+ | Hotel Gallitzinberg | 173 | 8 |
Si potrebbe notare che il *conteggio nell'insieme di dati* non corrisponde al valore in `Total_Number_of_Reviews`. Non è chiaro se questo valore nell'insieme di dati rappresentasse il numero totale di recensioni che l'hotel aveva, ma non tutte sono state recuperate o qualche altro calcolo. `Total_Number_of_Reviews` non viene utilizzato nel modello a causa di questa non chiarezza.
@@ -324,19 +324,19 @@ Trattare le seguenti domande come attività di codifica e provare a rispondere s
Ci si potrebbe anche chiedere del valore `Average_Score` e perché a volte è diverso dal punteggio medio calcolato. Poiché non è possibile sapere perché alcuni valori corrispondano, ma altri hanno una differenza, in questo caso è più sicuro utilizzare i punteggi delle recensioni a disposizione per calcolare autonomamente la media. Detto questo, le differenze sono solitamente molto piccole, ecco gli hotel con la maggiore deviazione dalla media dell'insieme di dati e dalla media calcolata:
- | Average_Score_Difference | Average_Score | Calc_Average_Score | Hotel_Name (Nome Hotel) |
- | :----------------------: | :-----------: | :----------------: | ------------------------------------------: |
- | -0,8 | 7,7 | 8,5 | Best Western Hotel Astoria |
- | -0,7 | 8,8 | 9,5 | Hotel Stendhal Place Vend me Parigi MGallery |
- | -0,7 | 7,5 | 8.2 | Mercure Paris Porte d'Orléans |
- | -0,7 | 7,9 | 8,6 | Renaissance Paris Vendome Hotel |
- | -0,5 | 7,0 | 7,5 | Hotel Royal Elys es |
- | ... | ... | ... | ... |
- | 0.7 | 7,5 | 6.8 | Mercure Paris Op ra Faubourg Montmartre |
- | 0,8 | 7,1 | 6.3 | Holiday Inn Paris Montparnasse Pasteur |
- | 0,9 | 6.8 | 5,9 | Villa Eugenia |
- | 0,9 | 8,6 | 7,7 | MARCHESE Faubourg St Honor Relais Ch teaux |
- | 1,3 | 7,2 | 5,9 | Kube Hotel Ice Bar |
+ | Average_Score_Difference | Average_Score | Calc_Average_Score | Hotel_Name (Nome Hotel) |
+ | :----------------------: | :-----------: | :----------------: | -------------------------------------------: |
+ | -0,8 | 7,7 | 8,5 | Best Western Hotel Astoria |
+ | -0,7 | 8,8 | 9,5 | Hotel Stendhal Place Vend me Parigi MGallery |
+ | -0,7 | 7,5 | 8.2 | Mercure Paris Porte d'Orléans |
+ | -0,7 | 7,9 | 8,6 | Renaissance Paris Vendome Hotel |
+ | -0,5 | 7,0 | 7,5 | Hotel Royal Elys es |
+ | ... | ... | ... | ... |
+ | 0.7 | 7,5 | 6.8 | Mercure Paris Op ra Faubourg Montmartre |
+ | 0,8 | 7,1 | 6.3 | Holiday Inn Paris Montparnasse Pasteur |
+ | 0,9 | 6.8 | 5,9 | Villa Eugenia |
+ | 0,9 | 8,6 | 7,7 | MARCHESE Faubourg St Honor Relais Ch teaux |
+ | 1,3 | 7,2 | 5,9 | Kube Hotel Ice Bar |
Con un solo hotel con una differenza di punteggio maggiore di 1, significa che probabilmente si può ignorare la differenza e utilizzare il punteggio medio calcolato.
@@ -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://jolly-sea-0a877260f.azurestaticapps.net/quiz/38/)
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/38/)
## Revisione e Auto Apprendimento
diff --git a/6-NLP/5-Hotel-Reviews-2/README.md b/6-NLP/5-Hotel-Reviews-2/README.md
index 12b9a15af08406c748139a3ad6989f90e72b3c42..c764b8e1cee3a432ac7d032529e58d4506042302 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 a 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/39/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.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://jolly-sea-0a877260f.azurestaticapps.net/quiz/40/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/40/)
## Challenge
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 f6d22b563ab96647438542a838df36643b1ed89c..46e92c7b646e864c2470e351f08e0b8065562938 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/39/)
+## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/39/)
### Operazioni di Filtraggio e Analisi del Sentiment
@@ -59,14 +59,14 @@ Occorre pulire un po' di più i dati. Si aggiungono colonne che saranno utili in
display(df.groupby("Hotel_Address").agg({"Hotel_Name": "nunique"}))
```
- | Hotel_Address | Hotel_Name (Nome Hotel) |
- | :--------------------- | :--------: |
- | Amsterdam, Paesi Bassi | 105 |
- | Barcellona, Spagna | 211 |
- | Londra, Regno Unito | 400 |
- | Milano, Italia | 162 |
- | Parigi, Francia | 458 |
- | Vienna, Austria | 158 |
+ | Hotel_Address | Hotel_Name (Nome Hotel) |
+ | :--------------------- | :---------------------: |
+ | Amsterdam, Paesi Bassi | 105 |
+ | Barcellona, Spagna | 211 |
+ | Londra, Regno Unito | 400 |
+ | Milano, Italia | 162 |
+ | Parigi, Francia | 458 |
+ | Vienna, Austria | 158 |
2. Elaborazione colonne di meta-recensione dell'hotel
@@ -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://jolly-sea-0a877260f.azurestaticapps.net/quiz/40/)
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/40/)
## Sfida
diff --git a/7-TimeSeries/1-Introduction/README.md b/7-TimeSeries/1-Introduction/README.md
index ae2a69e1ca35ffc4c988b066483bcd666bc200af..1d75763f0bb671c1065f93dc8e8c3b5c825ca95e 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/41/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.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://jolly-sea-0a877260f.azurestaticapps.net/quiz/42/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/42/)
## Review & Self Study
diff --git a/7-TimeSeries/2-ARIMA/README.md b/7-TimeSeries/2-ARIMA/README.md
index 3134d9392f4d58670bdef7b0d078a0b31a8b57c0..19da5622169e02c8686719d03e5884020c14988c 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/43/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.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://jolly-sea-0a877260f.azurestaticapps.net/quiz/44/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/44/)
## Review & Self Study
diff --git a/8-Reinforcement/1-QLearning/README.md b/8-Reinforcement/1-QLearning/README.md
index 6301c46e125c332157ca51eb47063bdfc7b70640..fbba835230e167e2acc9a7965eec52d2452af71b 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/45/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.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://jolly-sea-0a877260f.azurestaticapps.net/quiz/46/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/46/)
## Assignment [A More Realistic World](assignment.md)
diff --git a/8-Reinforcement/2-Gym/README.md b/8-Reinforcement/2-Gym/README.md
index b1a2e2a22e849eae45c9a9096fc816bc2a69791b..933967fe7fafe8dc247d4c4cba01c8f5d1b54406 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**.
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/47/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.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://jolly-sea-0a877260f.azurestaticapps.net/quiz/48/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/48/)
## Assignment: [Train a Mountain Car](assignment.md)
diff --git a/9-Real-World/1-Applications/README.md b/9-Real-World/1-Applications/README.md
index 154c57d6df4790886047ccc36b2a50de537ddaa9..b06b8b71676047bfd5ad3f586f81ebae626ef3f6 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/49/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.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://jolly-sea-0a877260f.azurestaticapps.net/quiz/50/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/50/)
## Review & Self Study
diff --git a/TRANSLATIONS.md b/TRANSLATIONS.md
index 22b0d37ad449aa7af37d3c296b66113168779e33..01f2f139564f9458a51c4b022c12a36c30f866fc 100644
--- a/TRANSLATIONS.md
+++ b/TRANSLATIONS.md
@@ -26,7 +26,7 @@ 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://https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1 becomes https://jolly-sea-0a877260f.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://white-water-09ec41f0f.azurestaticapps.net/quiz/1 becomes https://white-water-09ec41f0f.azurestaticapps.net/quiz/1?loc=id
**THANK YOU**