diff --git a/2-Regression/2-Data/README.md b/2-Regression/2-Data/README.md
index a647eb6b..135791e6 100644
--- a/2-Regression/2-Data/README.md
+++ b/2-Regression/2-Data/README.md
@@ -6,7 +6,7 @@ Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
 
 ## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/11/)
 
-> ### [This lesson is available in R!](./solution/lesson_2-R.ipynb)
+> ### [This lesson is available in R!](./solution/R/lesson_2-R.ipynb)
 
 ## Introduction
 
diff --git a/2-Regression/3-Linear/README.md b/2-Regression/3-Linear/README.md
index 0a4e09b4..c2fe1389 100644
--- a/2-Regression/3-Linear/README.md
+++ b/2-Regression/3-Linear/README.md
@@ -3,6 +3,8 @@
 ![Linear vs polynomial regression infographic](./images/linear-polynomial.png)
 > Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
 ## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/13/)
+
+> ### [This lesson is available in R!](./solution/R/lesson_3-R.ipynb)
 ### Introduction 
 
 So far you have explored what regression is with sample data gathered from the pumpkin pricing dataset that we will use throughout this lesson. You have also visualized it using Matplotlib. 
diff --git a/2-Regression/4-Logistic/README.md b/2-Regression/4-Logistic/README.md
index afc9c629..9ff52164 100644
--- a/2-Regression/4-Logistic/README.md
+++ b/2-Regression/4-Logistic/README.md
@@ -4,6 +4,8 @@
 > Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
 ## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/)
 
+> ### [This lesson is available in R!](./solution/R/lesson_4-R.ipynb)
+
 ## Introduction
 
 In this final lesson on Regression, one of the basic _classic_ ML techniques, we will take a look at Logistic Regression. You would use this technique to discover patterns to predict binary categories. Is this candy chocolate or not? Is this disease contagious or not? Will this customer choose this product or not? 
diff --git a/4-Classification/1-Introduction/README.md b/4-Classification/1-Introduction/README.md
index b6dde02f..03b0ba97 100644
--- a/4-Classification/1-Introduction/README.md
+++ b/4-Classification/1-Introduction/README.md
@@ -21,6 +21,8 @@ Classification uses various algorithms to determine other ways of determining a
 
 ## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/19/)
 
+> ### [This lesson is available in R!](./solution/R/lesson_10-R.ipynb)
+
 ### Introduction
 
 Classification is one of the fundamental activities of the machine learning researcher and data scientist. From basic classification of a binary value ("is this email spam or not?"), to complex image classification and segmentation using computer vision, it's always useful to be able to sort data into classes and ask questions of it.