Update README.md

Author: tirthajyoti

README.md

@@ -4,9 +4,10 @@ Essential codes for jump-starting machine learning/data science with Python
 * Jupyter notebooks covering a wide range of functions and operations on the topics of NumPy, Pandans, Seaborn, matplotlib etc.
 
 ### Tutorial-type notebooks covering regression, classification, clustering, and some basic neural network algorithms
-* Simple linear regression
+* Simple linear regression with t-statistic generation
+* Multiple ways to do linear regression in Python and their speed comparison ([check the article I wrote on freeCodeCamp] (https://medium.freecodecamp.org/data-science-with-python-8-ways-to-do-linear-regression-and-measure-their-speed-b5577d75f8b))
 * Multi-variate regression with regularization
-* Polynomial regression with how to use scikit-learn pipeline feature
+* Polynomial regression with how to use scikit-learn pipeline feature ([check the article I wrote on _Towards Data Science_](https://towardsdatascience.com/machine-learning-with-python-easy-and-robust-method-to-fit-nonlinear-data-19e8a1ddbd49))
 * Logistic regression/classification
 * _k_-nearest neighbor classification
 * Decision trees and Random Forest