From 66d48d04aa75b314745829e31391b2071f931829 Mon Sep 17 00:00:00 2001
From: p-mishra1 <87666586+p-mishra1@users.noreply.github.com>
Date: Thu, 18 Nov 2021 21:30:17 +0530
Subject: [PATCH] Update README.md

---
 README.md | 50 +++++++++++++++++++++++++-------------------------
 1 file changed, 25 insertions(+), 25 deletions(-)

diff --git a/README.md b/README.md
index f0d72a89..dec50211 100644
--- a/README.md
+++ b/README.md
@@ -81,31 +81,31 @@ By ensuring that the content aligns with projects, the process is made more enga
 
 | 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                        |
+|  <li>- [x] 01</li>          |                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                       |
+|     <li>- [x] 02</li>       |                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                      |
+|      <li>- [ ] 03</li>       |                 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                        |
+|      <li>- [ ] 04</li>       |                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                     |
+|      <li>- [ ] 05</li>       |                   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>       |
+|      <li>- [ ] 06</li>       |                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>       |
+|      <li>- [] 07</li>       |                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>       |
+|      <li>- [ ] 08</li>       |                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>       |
+|      <li>- [ ] 09</li>       |                          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                          |
+|      <li>- [ ] 10</li>       |                 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> |
+|      <li>- [ ] 11</li>       |             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> |
+|      <li>- [ ] 12</li>       |             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> |
+|      <li>- [ ] 13</li>       |             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                          |
+|      <li>- [ ] 14</li>       |                   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>       |
+|      <li>- [ ] 15</li>       |              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>       |
+|      <li>- [ ] 16</li>       |        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                        |
+|      <li>- [ ] 17</li>       |                      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                        |
+|      <li>- [ ] 18</li>       |             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                        |
+|      <li>- [ ] 19</li>       |                  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                        |
+|       <li>- [ ] 20</li>      |                  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                        |
+|      <li>- [ ] 21</li>       |            Introduction to time series forecasting             |        [Time series](7-TimeSeries/README.md)        | Introduction to time series forecasting                                                                                         |                                             [Python](7-TimeSeries/1-Introduction/README.md)                                              |                      Francesca                       |
+|      <li>- [ ] 22</li>       | ⚡️ 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                       |
+|      <li>- [ ] 23</li>       |  ⚡️ 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                        |
+|      <li>- [ ] 24</li>       |             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                        |
+|      <li>- [ ] 25</li>       |                 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                         |
 
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