video callouts and clustering edits

Classification/1-Data/README.md

 # Introduction to Classification
 
 [![Introduction to Classification](https://img.youtube.com/vi/eg8DJYwdMyg/0.jpg)](https://youtu.be/eg8DJYwdMyg "Introduction to Classification")
-> MIT's John Guttag introduces Classification
+> 🎥 Click the image above for a video: MIT's John Guttag introduces Classification
 
 ## [Pre-lecture quiz](link-to-quiz-app)
 

Clustering/1-Visualize/README.md

@@ -9,7 +9,7 @@ Clustering is a type of [Unsupervised Learning](https://wikipedia.org/wiki/Unsup
 ### Introduction
 
 [![Introduction to ML](https://img.youtube.com/vi/esmzYhuFnds/0.jpg)](https://youtu.be/esmzYhuFnds "Introduction to Clustering")
-> MIT's John Guttag introduces Clustering
+🎥 Click the image above for a video: MIT's John Guttag introduces Clustering
 
 [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.
 
@@ -309,6 +309,4 @@ Before you apply clustering algorithms, as we have learned, it's a good idea to
 
 [This helpful article](https://www.freecodecamp.org/news/8-clustering-algorithms-in-machine-learning-that-all-data-scientists-should-know/) walks you through the different ways that various clustering algorithms behave, given different data shapes.
 
-In the next lesson, you will make use of the  most popular clustering method, K-Means. Take a look at Stanford's K-Means Simulator [here](https://stanford.edu/class/engr108/visualizations/kmeans/kmeans.html). You can use this tool to visualize sample data points and determine its centroids. With fresh data, click 'update' to see how long it takes to find convergence. You can edit the data's randomness, numbers of clusters and numbers of centroids. Does this help you get an idea of how the data can be grouped?
-
 **Assignment**: [Research other visualizations for clustering](assignment.md)

Clustering/2-K-Means/README.md

-# [Lesson Topic]
-
-Add a sketchnote if possible/appropriate
+# K-Means Clustering
 
 ![Embed a video here if available](video-url)
 https://www.youtube.com/watch?v=hDmNF9JG3lo
@@ -12,46 +10,24 @@ Describe what we will learn
 
 ### Introduction
 
-Describe what will be covered
-
-> Notes
-
 ### Prerequisite
-
-What steps should have been covered before this lesson?
-
 ### Preparation
 
 Preparatory steps to start this lesson
 
----
-
-[Step through content in blocks]
 
-## [Topic 1]
-
-### Task:
-
-Work together to progressively enhance your codebase to build the project with shared code:
-
-```html
-code blocks
-```
 
 ✅ Knowledge Check - use this moment to stretch students' knowledge with open questions
 
-## [Topic 2]
-
-## [Topic 3]
 
 ## 🚀Challenge
 
-Add a challenge for students to work on collaboratively in class to enhance the project
 
-Optional: add a screenshot of the completed lesson's UI if appropriate
 
 ## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/28/)
 
 ## Review & Self Study
 
+Take a look at Stanford's K-Means Simulator [here](https://stanford.edu/class/engr108/visualizations/kmeans/kmeans.html). You can use this tool to visualize sample data points and determine its centroids. With fresh data, click 'update' to see how long it takes to find convergence. You can edit the data's randomness, numbers of clusters and numbers of centroids. Does this help you get an idea of how the data can be grouped?
+
 **Assignment**: [Assignment Name](assignment.md)

Introduction/1-intro-to-ML/README.md

@@ -2,14 +2,14 @@
 
 [![ML, AI, Deep Learning - What's the difference?](https://img.youtube.com/vi/lTd9RSxS9ZE/0.jpg)](https://youtu.be/lTd9RSxS9ZE "ML, AI, Deep Learning - What's the difference?")
 
-> Click this image to watch a video discussing the difference between Machine Learning, AI, and Deep Learning.
+> 🎥 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/)
 ### Introduction
 
 Describe what will be covered
 
 [![Introduction to ML](https://img.youtube.com/vi/h0e2HAPTGF4/0.jpg)](https://youtu.be/h0e2HAPTGF4 "Introduction to ML")
-> MIT's John Guttag introduces Machine Learning
+> 🎥 Click the image above for a video: MIT's John Guttag introduces Machine Learning
 ### Preparation
 
 Before embarking on this curriculum, you need to have your computer set up and ready to run notebooks locally. Learn more about how to do this in this [set of videos](https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6)

Introduction/2-history-of-ML/README.md

@@ -47,7 +47,7 @@ Eliza, an early 'chatterbot', could converse w/ people and act as a primitive 't
 "Blocks world" was an example of a micro world where blocks can be stacked and sorted and experiments in teaching machines to make decisions could be tested. Advances built with libraries such as [SHRDLU](https://wikipedia.org/wiki/SHRDLU) helped propel language processing forward. 
 
 [![blocks world with SHRDLU](https://img.youtube.com/vi/QAJz4YKUwqw/0.jpg)](https://www.youtube.com/watch?v=QAJz4YKUwqw "blocks world with SHRDLU")
-> Blocks world with SHRDLU
+> 🎥 Click the image above for a video: Blocks world with SHRDLU
 
 ## 1974 - 1980: "AI Winter"
 
@@ -80,7 +80,7 @@ This epoch saw a new era for ML and AI to be able to solve some of the problems
 Today, Machine Learning and AI touch almost every part of our lives. This era calls for careful understanding of the risks and potentials effects of these algorithms on human lives. As Microsoft's Brad Smith has stated, "Information technology raises issues that go to the heart of fundamental human-rights protections like privacy and freedom of expression. These issues heighten responsibility for tech companies that create these products. In our view, they also call for thoughtful government regulation and for the development of norms around acceptable uses."[source](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/). It remains to be seen what the future holds but it is important to understand these computer systems and the software and algorithms that they run. We hope that this curriculum will help you to gain a better understanding so that you can decide for yourself.
 
 [![The history of Deep Learning](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 "The history of Deep Learning")
-> Yann LeCun discusses the history of Deep Learning in this lecture
+> 🎥 Click the image above for a video: Yann LeCun discusses the history of Deep Learning in this lecture
 ## 🚀Challenge
 
 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?

Introduction/3-fairness/README.md

@@ -24,7 +24,7 @@ As a prerequisite, please take the "Responsible AI Principles" Learn Path and wa
 Learn more about Responsible AI by following this [Learning Path](https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/?WT.mc_id=academic-15963-cxa)
 
 [![Microsoft's Approach to Responsible AI](https://img.youtube.com/vi/dnC8-uUZXSc/0.jpg)](https://youtu.be/dnC8-uUZXSc "Microsoft's Approach to Responsible AI")
-> Video: Microsoft's Approach to Responsible AI
+> 🎥 Click the image above for a video: Microsoft's Approach to Responsible AI
  
 ## Unfairness in data and algorithms 
  
@@ -71,7 +71,7 @@ Stereotypical gender view was found in machine translation. When translating “
 An image labeling technology infamously mislabeled images of dark-skinned people as gorillas. Mislabeling is harmful not just because the system made a mistake because it specifically applied a label that has a long history of being purposefully used to denigrate Black people.
 
 [![AI: Ain't I a Woman?](https://img.youtube.com/vi/QxuyfWoVV98/0.jpg)](https://www.youtube.com/watch?v=QxuyfWoVV98 "AI, Ain't I a Woman?")
-> Video: AI, Ain't I a Woman - a performance showing the harm caused by racist denigration by AI
+> 🎥 Click the image above for a video: AI, Ain't I a Woman - a performance showing the harm caused by racist denigration by AI
 ### Over- or under- representation 
  
 Skewed image search results can be a good example of this harm. When searching images of professions with an equal or higher percentage of men than women, such as engineering, or CEO, watch for results that are more heavily skewed towards a given gender.

README.md

@@ -41,7 +41,7 @@ Travel with us around the world as we apply these classic techniques to data fro
 > Future space for Promo Video
 [![Promo video](screenshot.png)](https://youtube.com/watch?v=R1wrdtmBSII "Promo video")
 
-> Click the image above for a video about the project and the folks who created it!
+> 🎥 Click the image above for a video about the project and the folks who created it!
 
 ## Pedagogy
 

Regression/1-Tools/README.md

@@ -22,7 +22,7 @@ In this lesson, you will learn:
 
 [![Using Python with Visual Studio Code](https://img.youtube.com/vi/7EXd4_ttIuw/0.jpg)](https://youtu.be/7EXd4_ttIuw "Using Python with Visual Studio Code")
 
-> Click this image to watch a video on using Python within VS Code.
+> 🎥 Click the image above for a video: using Python within VS Code.
 
 1. Ensure that [Python](https://www.python.org/downloads/) is installed on your computer. You will use Python for many data science and machine learning tasks. Most computer systems already include a Python installation. There are useful [Python Coding Packs](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-15963-cxa) available as well to ease the setup for some users. Some usages of Python, however, require one version of the software, whereas others require a different version. For this reason, it's useful to work within a virtual environment.