Updated index.md (#22280)

* Updated index.md

Added more resources to the article

* Updated index.md

* Added more resources to the article

Moved the lines out of the code block as requested

guide/english/machine-learning/linear-regression/index.md

@@ -37,6 +37,8 @@ def gradient_descent_runner(points, starting_b, starting_m, learning_rate, num_i
     return [b, m]
 
 gradient_descent_runner(wheat_and_bread, 1, 1, 0.01, 100)
+
+
 ```
 
 Code example is from <a href='http://blog.floydhub.com/coding-the-history-of-deep-learning/' target='_blank' rel='nofollow'>this article</a>. It also explains gradient descent and other essential concepts for deep learning.
@@ -66,3 +68,7 @@ print(model.intercept_)
 from sklearn import metrics
 print(metrics.accuracy_score(y_test, y_pred_class))
 ```
+
+
+You can refer to this article for deeper insight into regression  
+https://www.analyticsvidhya.com/blog/2017/06/a-comprehensive-guide-for-linear-ridge-and-lasso-regression/

guide/english/machine-learning/logistic-regression/index.md

@@ -36,6 +36,9 @@ penalizing the learning algorithms are used.
 Cost(hθ(x),y)=−log(hθ(x))           if y = 1
 Cost(hθ(x),y)=−log(1−hθ(x))         if y = 0
 
+Refer to this article for clearing your basics https://www.analyticsvidhya.com/blog/2017/06/a-comprehensive-guide-for-linear-ridge-and-lasso-regression/
+
+
 #### Predictions using logistic regression:
 Logistic regression models the probability of the default class (i.e. the first class).
 You can classify results given by: