diff --git a/README.md b/README.md
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 |      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.html)</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.html)</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.html)</li></ul>        |      <ul><li>Jen and Dmitry</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.html)</li></ul>      |      <ul><li>Jen</li><li>Eric Wanjau</li></ul>       |
+|      05       |                   Introduction to regression                   |        [Regression](2-Regression/README.md)         | Get started with Python and Scikit-learn for regression models                                                                  |         [Python](2-Regression/1-Tools/README.md) • [R](2-Regression/1-Tools/solution/R/lesson_1.html)         |      Jen • Eric Wanjau       |
+|      06       |                North American pumpkin prices 🎃                |        [Regression](2-Regression/README.md)         | Visualize and clean data in preparation for ML                                                                                  |          [Python](2-Regression/2-Data/README.md) • [R](2-Regression/2-Data/solution/R/lesson_2.html)          |      Jen • Eric Wanjau       |
+|      07       |                North American pumpkin prices 🎃                |        [Regression](2-Regression/README.md)         | Build linear and polynomial regression models                                                                                   |        [Python](2-Regression/3-Linear/README.md) • [R](2-Regression/3-Linear/solution/R/lesson_3.html)        |      Jen and Dmitry • Eric Wanjau       |
+|      08       |                North American pumpkin prices 🎃                |        [Regression](2-Regression/README.md)         | Build a logistic regression model                                                                                               |     [Python](2-Regression/4-Logistic/README.md) • [R](2-Regression/4-Logistic/solution/R/lesson_4.html)      |      Jen • Eric Wanjau       |
 |      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.html)  | <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.html) | <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.html) | <ul><li>Jen and Cassie</li><li>Eric Wanjau</li></ul> |
+|      10       |                 Introduction to classification                 |    [Classification](4-Classification/README.md)     | Clean, prep, and visualize your data; introduction to classification                                                            | [Python](4-Classification/1-Introduction/README.md) • [R](4-Classification/1-Introduction/solution/R/lesson_10.html)  | Jen and Cassie • Eric Wanjau |
+|      11       |             Delicious Asian and Indian cuisines 🍜             |    [Classification](4-Classification/README.md)     | Introduction to classifiers                                                                                                     | [Python](4-Classification/2-Classifiers-1/README.md) • [R](4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | Jen and Cassie • Eric Wanjau |
+|      12       |             Delicious Asian and Indian cuisines 🍜             |    [Classification](4-Classification/README.md)     | More classifiers                                                                                                                | [Python](4-Classification/3-Classifiers-2/README.md) • [R](4-Classification/3-Classifiers-2/solution/R/lesson_12.html) | Jen and Cassie • Eric Wanjau |
 |      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.html)         |      <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.html)           |      <ul><li>Jen</li><li>Eric Wanjau</li></ul>       |
+|      14       |                   Introduction to clustering                   |        [Clustering](5-Clustering/README.md)         | Clean, prep, and visualize your data; Introduction to clustering                                                                |         [Python](5-Clustering/1-Visualize/README.md) • [R](5-Clustering/1-Visualize/solution/R/lesson_14.html)         |      Jen • Eric Wanjau       |
+|      15       |              Exploring Nigerian Musical Tastes 🎧              |        [Clustering](5-Clustering/README.md)         | Explore the K-Means clustering method                                                                                           |           [Python](5-Clustering/2-K-Means/README.md) • [R](5-Clustering/2-K-Means/solution/R/lesson_15.html)           |      Jen • Eric Wanjau       |
 |      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                        |