From 05c9381e8c8289f119b7e71243ea24e6114a1616 Mon Sep 17 00:00:00 2001
From: Priyank Khanna <55731241+priyankkhanna@users.noreply.github.com>
Date: Sat, 28 Aug 2021 01:21:07 +0530
Subject: [PATCH] corrected R links

---
 README.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index 086fd4aa..a496632e 100644
--- a/README.md
+++ b/README.md
@@ -81,10 +81,10 @@ By ensuring that the content aligns with projects, the process is made more enga
 |       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_1R.md)</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_2R.md)</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_3R.md)</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_4R.md)</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                                                                  |       <ul><li>[Python](2-Regression/1-Tools/README.md)</li><li>[R](2-Regression/1-Tools/solution/R/lesson_1R.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_2R.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_3R.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_4R.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                                                            |  [Python](4-Classification/1-Introduction/README.md)  | Jen and Cassie |
 |       11      |            Delicious Asian and Indian cuisines 🍜           |     [Classification](4-Classification/README.md)    | Introduction to classifiers                                                                                                     |  [Python](4-Classification/2-Classifiers-1/README.md)  | Jen and Cassie |