diff --git a/README.md b/README.md
index 738afd2f..f0d72a89 100644
--- a/README.md
+++ b/README.md
@@ -22,7 +22,7 @@ Travel with us around the world as we apply these classic techniques to data fro
 
 **🙏 Special thanks 🙏 to our Microsoft Student Ambassador authors, reviewers, and content contributors**, notably Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, and Snigdha Agarwal
 
-**🤩 Extra gratitude to Microsoft Student Ambassador Eric Wanjau for our R lessons!** 
+**🤩 Extra gratitude to Microsoft Student Ambassador Eric Wanjau for our R lessons!**
 
 ---
 
@@ -46,7 +46,9 @@ Travel with us around the world as we apply these classic techniques to data fro
 
 ## Meet the Team
 
-[![Promo video](ml-for-beginners.png)](https://youtu.be/Tj1XWrDSYJU "Promo video")
+[![Promo video](ml.gif)](https://youtu.be/Tj1XWrDSYJU "Promo video")
+
+**Gif by** [Mohit Jaisal](https://linkedin.com/in/mohitjaisal)
 
 > 🎥 Click the image above for a video about the project and the folks who created it!
 
@@ -77,34 +79,34 @@ By ensuring that the content aligns with projects, the process is made more enga
 
 > **A note about quizzes**: All quizzes are contained [in this app](https://white-water-09ec41f0f.azurestaticapps.net/), for 52 total quizzes of three questions each. They are linked from within the lessons but the quiz app can be run locally; follow the instruction in the `quiz-app` folder.
 
-| Lesson Number |                            Topic                           |                   Lesson Grouping                   | Learning Objectives                                                                                                             |                   Linked Lesson                  |     Author     |
-|:-------------:|:----------------------------------------------------------:|:---------------------------------------------------:|---------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------:|:--------------:|
-|       01      |              Introduction to machine learning              |       [Introduction](1-Introduction/README.md)      | Learn the basic concepts behind machine learning                                                                                |    [Lesson](1-Introduction/1-intro-to-ML/README.md)           |    Muhammad    |
-|       02      |               The History of machine learning              |       [Introduction](1-Introduction/README.md)      | Learn the history underlying this field                                                                                         |   [Lesson](1-Introduction/2-history-of-ML/README.md)          |   Jen and Amy  |
-|       03      |                Fairness and machine learning               |       [Introduction](1-Introduction/README.md)      | What are the important philosophical issues around fairness that students should consider when building and applying ML models? |     [Lesson](1-Introduction/3-fairness/README.md)             |     Tomomi     |
-|       04      |               Techniques for machine learning              |       [Introduction](1-Introduction/README.md)      | What techniques do ML researchers use to build ML models?                                                                       | [Lesson](1-Introduction/4-techniques-of-ML/README.md)          |  Chris and Jen |
-|       05      |                 Introduction to regression                 |         [Regression](2-Regression/README.md)        | Get started with Python and Scikit-learn for regression models                                                                  |       <ul><li>[Python](2-Regression/1-Tools/README.md)</li><li>[R](2-Regression/1-Tools/solution/R/lesson_1-R.ipynb)</li></ul>       |       <ul><li>Jen</li><li>Eric Wanjau</li></ul>    |
-|       06      |               North American pumpkin prices 🎃              |         [Regression](2-Regression/README.md)        | Visualize and clean data in preparation for ML                                                                                  |     <ul><li>[Python](2-Regression/2-Data/README.md)</li><li>[R](2-Regression/2-Data/solution/R/lesson_2-R.ipynb)</li></ul>         |        <ul><li>Jen</li><li>Eric Wanjau</li></ul>     |
-|       07      |               North American pumpkin prices 🎃              |         [Regression](2-Regression/README.md)        | Build linear and polynomial regression models                                                                                   |     <ul><li>[Python](2-Regression/3-Linear/README.md)</li><li>[R](2-Regression/3-Linear/solution/R/lesson_3-R.ipynb)</li></ul>       |       <ul><li>Jen</li><li>Eric Wanjau</li></ul>     |
-|       08      |               North American pumpkin prices 🎃              |         [Regression](2-Regression/README.md)        | Build a logistic regression model                                                                                               |     <ul><li>[Python](2-Regression/4-Logistic/README.md)  </li><li>[R](2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb)</li></ul>     |       <ul><li>Jen</li><li>Eric Wanjau</li></ul>     |
-|       09      |                         A Web App 🔌                        |            [Web App](3-Web-App/README.md)           | Build a web app to use your trained model                                                                                       |        [Python](3-Web-App/1-Web-App/README.md)       |       Jen      |
-|       10     |               Introduction to classification               |     [Classification](4-Classification/README.md)    | Clean, prep, and visualize your data; introduction to classification                                                            |<ul><li>  [Python](4-Classification/1-Introduction/README.md) </li><li>[R](4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb)  | <ul><li>Jen and Cassie</li><li>Eric Wanjau</li></ul> |
-|       11      |            Delicious Asian and Indian cuisines 🍜           |     [Classification](4-Classification/README.md)    | Introduction to classifiers                                                                                                     |<ul><li> [Python](4-Classification/2-Classifiers-1/README.md)</li><li>[R](4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb) | <ul><li>Jen and Cassie</li><li>Eric Wanjau</li></ul> |
-|       12      |            Delicious Asian and Indian cuisines 🍜           |     [Classification](4-Classification/README.md)    | More classifiers                                                                                                                |<ul><li>  [Python](4-Classification/3-Classifiers-2/README.md)</li><li>[R](4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb)  | <ul><li>Jen and Cassie</li><li>Eric Wanjau</li></ul> |
-|       13      |            Delicious Asian and Indian cuisines 🍜           |     [Classification](4-Classification/README.md)    | Build a recommender web app using your model                                                                                    |     [Python](4-Classification/4-Applied/README.md)     |       Jen      |
-|       14      |                 Introduction to clustering                 |         [Clustering](5-Clustering/README.md)        | Clean, prep, and visualize your data; Introduction to clustering                                                                |     <ul><li> [Python](5-Clustering/1-Visualize/README.md)</li><li>[R](5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb)    |       <ul><li>Jen</li><li>Eric Wanjau</li></ul>    |
-|       15      |             Exploring Nigerian Musical Tastes 🎧            |         [Clustering](5-Clustering/README.md)        | Explore the K-Means clustering method                                                                                           |      <ul><li> [Python](5-Clustering/2-K-Means/README.md)</li><li>[R](5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb)       |       <ul><li>Jen</li><li>Eric Wanjau</li></ul>      |
-|       16      |        Introduction to natural language processing ☕️       |    [Natural language processing](6-NLP/README.md)   | Learn the basics about NLP by building a simple bot                                                                             |    [Python](6-NLP/1-Introduction-to-NLP/README.md)  |     Stephen    |
-|       17      |                     Common NLP Tasks ☕️                     |    [Natural language processing](6-NLP/README.md)   | Deepen your NLP knowledge by understanding common tasks required when dealing with language structures                          |           [Python](6-NLP/2-Tasks/README.md)     |     Stephen    |
-|       18      |            Translation and sentiment analysis ♥️            |    [Natural language processing](6-NLP/README.md)   | Translation and sentiment analysis with Jane Austen                                                                             |   [Python](6-NLP/3-Translation-Sentiment/README.md) |     Stephen    |
-|       19      |                 Romantic hotels of Europe ♥️                |    [Natural language processing](6-NLP/README.md)   | Sentiment analysis with hotel reviews 1                                                                                         |      [Python](6-NLP/4-Hotel-Reviews-1/README.md)    |     Stephen    |
-|       20      |                 Romantic hotels of Europe ♥️                |    [Natural language processing](6-NLP/README.md)   | Sentiment analysis with hotel reviews 2                                                                                         |      [Python](6-NLP/5-Hotel-Reviews-2/README.md)   |     Stephen    |
-|       21      |           Introduction to time series forecasting          |        [Time series](7-TimeSeries/README.md)        | Introduction to time series forecasting                                                                                         |    [Python](7-TimeSeries/1-Introduction/README.md)    |    Francesca   |
-|       22      | ⚡️ World Power Usage ⚡️ - time series forecasting with ARIMA |        [Time series](7-TimeSeries/README.md)        | Time series forecasting with ARIMA                                                                                              |        [Python](7-TimeSeries/2-ARIMA/README.md)        |    Francesca   |
-|       23      | ⚡️ World Power Usage ⚡️ - time series forecasting with SVR |        [Time series](7-TimeSeries/README.md)        | Time series forecasting with Support Vector Regressor                                                                                              |        [Python](7-TimeSeries/3-SVR/README.md)        |    Anirban   |
-|       24      |           Introduction to reinforcement learning           | [Reinforcement learning](8-Reinforcement/README.md) | Introduction to reinforcement learning with Q-Learning                                                                          |    [Python](8-Reinforcement/1-QLearning/README.md)  |     Dmitry     |
-|       25      |                Help Peter avoid the wolf! 🐺                | [Reinforcement learning](8-Reinforcement/README.md) | Reinforcement learning Gym                                                                                                      |       [Python](8-Reinforcement/2-Gym/README.md)      |     Dmitry     |
-|   Postscript  |          Real-World ML scenarios and applications          |       [ML in the Wild](9-Real-World/README.md)      | Interesting and revealing real-world applications of classical ML                                                               |    [Lesson](9-Real-World/1-Applications/README.md)    |      Team      |
+| Lesson Number |                             Topic                              |                   Lesson Grouping                   | Learning Objectives                                                                                                             |                                                              Linked Lesson                                                               |                        Author                        |
+| :-----------: | :------------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------: |
+|      01       |                Introduction to machine learning                |      [Introduction](1-Introduction/README.md)       | Learn the basic concepts behind machine learning                                                                                |                                             [Lesson](1-Introduction/1-intro-to-ML/README.md)                                             |                       Muhammad                       |
+|      02       |                The History of machine learning                 |      [Introduction](1-Introduction/README.md)       | Learn the history underlying this field                                                                                         |                                            [Lesson](1-Introduction/2-history-of-ML/README.md)                                            |                     Jen and Amy                      |
+|      03       |                 Fairness and machine learning                  |      [Introduction](1-Introduction/README.md)       | What are the important philosophical issues around fairness that students should consider when building and applying ML models? |                                              [Lesson](1-Introduction/3-fairness/README.md)                                               |                        Tomomi                        |
+|      04       |                Techniques for machine learning                 |      [Introduction](1-Introduction/README.md)       | What techniques do ML researchers use to build ML models?                                                                       |                                          [Lesson](1-Introduction/4-techniques-of-ML/README.md)                                           |                    Chris and Jen                     |
+|      05       |                   Introduction to regression                   |        [Regression](2-Regression/README.md)         | Get started with Python and Scikit-learn for regression models                                                                  |         <ul><li>[Python](2-Regression/1-Tools/README.md)</li><li>[R](2-Regression/1-Tools/solution/R/lesson_1-R.ipynb)</li></ul>         |      <ul><li>Jen</li><li>Eric Wanjau</li></ul>       |
+|      06       |                North American pumpkin prices 🎃                |        [Regression](2-Regression/README.md)         | Visualize and clean data in preparation for ML                                                                                  |          <ul><li>[Python](2-Regression/2-Data/README.md)</li><li>[R](2-Regression/2-Data/solution/R/lesson_2-R.ipynb)</li></ul>          |      <ul><li>Jen</li><li>Eric Wanjau</li></ul>       |
+|      07       |                North American pumpkin prices 🎃                |        [Regression](2-Regression/README.md)         | Build linear and polynomial regression models                                                                                   |        <ul><li>[Python](2-Regression/3-Linear/README.md)</li><li>[R](2-Regression/3-Linear/solution/R/lesson_3-R.ipynb)</li></ul>        |      <ul><li>Jen</li><li>Eric Wanjau</li></ul>       |
+|      08       |                North American pumpkin prices 🎃                |        [Regression](2-Regression/README.md)         | Build a logistic regression model                                                                                               |     <ul><li>[Python](2-Regression/4-Logistic/README.md) </li><li>[R](2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb)</li></ul>      |      <ul><li>Jen</li><li>Eric Wanjau</li></ul>       |
+|      09       |                          A Web App 🔌                          |           [Web App](3-Web-App/README.md)            | Build a web app to use your trained model                                                                                       |                                                 [Python](3-Web-App/1-Web-App/README.md)                                                  |                         Jen                          |
+|      10       |                 Introduction to classification                 |    [Classification](4-Classification/README.md)     | Clean, prep, and visualize your data; introduction to classification                                                            | <ul><li> [Python](4-Classification/1-Introduction/README.md) </li><li>[R](4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb)  | <ul><li>Jen and Cassie</li><li>Eric Wanjau</li></ul> |
+|      11       |             Delicious Asian and Indian cuisines 🍜             |    [Classification](4-Classification/README.md)     | Introduction to classifiers                                                                                                     | <ul><li> [Python](4-Classification/2-Classifiers-1/README.md)</li><li>[R](4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb) | <ul><li>Jen and Cassie</li><li>Eric Wanjau</li></ul> |
+|      12       |             Delicious Asian and Indian cuisines 🍜             |    [Classification](4-Classification/README.md)     | More classifiers                                                                                                                | <ul><li> [Python](4-Classification/3-Classifiers-2/README.md)</li><li>[R](4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb) | <ul><li>Jen and Cassie</li><li>Eric Wanjau</li></ul> |
+|      13       |             Delicious Asian and Indian cuisines 🍜             |    [Classification](4-Classification/README.md)     | Build a recommender web app using your model                                                                                    |                                              [Python](4-Classification/4-Applied/README.md)                                              |                         Jen                          |
+|      14       |                   Introduction to clustering                   |        [Clustering](5-Clustering/README.md)         | Clean, prep, and visualize your data; Introduction to clustering                                                                |         <ul><li> [Python](5-Clustering/1-Visualize/README.md)</li><li>[R](5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb)         |      <ul><li>Jen</li><li>Eric Wanjau</li></ul>       |
+|      15       |              Exploring Nigerian Musical Tastes 🎧              |        [Clustering](5-Clustering/README.md)         | Explore the K-Means clustering method                                                                                           |           <ul><li> [Python](5-Clustering/2-K-Means/README.md)</li><li>[R](5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb)           |      <ul><li>Jen</li><li>Eric Wanjau</li></ul>       |
+|      16       |        Introduction to natural language processing ☕️         |   [Natural language processing](6-NLP/README.md)    | Learn the basics about NLP by building a simple bot                                                                             |                                             [Python](6-NLP/1-Introduction-to-NLP/README.md)                                              |                       Stephen                        |
+|      17       |                      Common NLP Tasks ☕️                      |   [Natural language processing](6-NLP/README.md)    | Deepen your NLP knowledge by understanding common tasks required when dealing with language structures                          |                                                    [Python](6-NLP/2-Tasks/README.md)                                                     |                       Stephen                        |
+|      18       |             Translation and sentiment analysis ♥️              |   [Natural language processing](6-NLP/README.md)    | Translation and sentiment analysis with Jane Austen                                                                             |                                            [Python](6-NLP/3-Translation-Sentiment/README.md)                                             |                       Stephen                        |
+|      19       |                  Romantic hotels of Europe ♥️                  |   [Natural language processing](6-NLP/README.md)    | Sentiment analysis with hotel reviews 1                                                                                         |                                               [Python](6-NLP/4-Hotel-Reviews-1/README.md)                                                |                       Stephen                        |
+|      20       |                  Romantic hotels of Europe ♥️                  |   [Natural language processing](6-NLP/README.md)    | Sentiment analysis with hotel reviews 2                                                                                         |                                               [Python](6-NLP/5-Hotel-Reviews-2/README.md)                                                |                       Stephen                        |
+|      21       |            Introduction to time series forecasting             |        [Time series](7-TimeSeries/README.md)        | Introduction to time series forecasting                                                                                         |                                             [Python](7-TimeSeries/1-Introduction/README.md)                                              |                      Francesca                       |
+|      22       | ⚡️ World Power Usage ⚡️ - time series forecasting with ARIMA |        [Time series](7-TimeSeries/README.md)        | Time series forecasting with ARIMA                                                                                              |                                                 [Python](7-TimeSeries/2-ARIMA/README.md)                                                 |                      Francesca                       |
+|      23       |  ⚡️ World Power Usage ⚡️ - time series forecasting with SVR  |        [Time series](7-TimeSeries/README.md)        | Time series forecasting with Support Vector Regressor                                                                           |                                                  [Python](7-TimeSeries/3-SVR/README.md)                                                  |                       Anirban                        |
+|      24       |             Introduction to reinforcement learning             | [Reinforcement learning](8-Reinforcement/README.md) | Introduction to reinforcement learning with Q-Learning                                                                          |                                             [Python](8-Reinforcement/1-QLearning/README.md)                                              |                        Dmitry                        |
+|      25       |                 Help Peter avoid the wolf! 🐺                  | [Reinforcement learning](8-Reinforcement/README.md) | Reinforcement learning Gym                                                                                                      |                                                [Python](8-Reinforcement/2-Gym/README.md)                                                 |                        Dmitry                        |
+|  Postscript   |            Real-World ML scenarios and applications            |      [ML in the Wild](9-Real-World/README.md)       | Interesting and revealing real-world applications of classical ML                                                               |                                             [Lesson](9-Real-World/1-Applications/README.md)                                              |                         Team                         |
 
 ## Offline access
 
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