Update README.md

Author: tirthajyoti

README.md

@@ -1,26 +1,33 @@
 # Python Machine Learning Notebooks
 Essential codes for jump-starting machine learning/data science with Python
 
-### Essential tutorial-type notebooks on Pandas and Numpy
+## Essential tutorial-type notebooks on Pandas and Numpy
 * 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
+## Tutorial-type notebooks covering regression, classification, clustering, and some basic neural network algorithms
+
+### 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*** ([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))
+* Decision trees and Random Forest regression (showing how the Random Forest works as a robust/regularized meta-estimator rejecting overfitting)
+
+### Classification
 * Logistic regression/classification
 * _k_-nearest neighbor classification
 * Decision trees and Random Forest Classification
-* Decision trees and Random Forest regression (showing how the Random Forest works as a robust/regularized meta-estimator rejecting overfitting)
 * Support vector machine classification
+* Naive Bayes classification
+
+### Clustering
 * _K_-means clustering
 
 ### Function approximation by linear model and Deep Learning method
 * Demo notebook to illustrate the superiority of deep neural network for complex nonlinear function approximation task.
 * Step-by-step building of 1-hidden-layer and 2-hidden-layer dense network using basic TensorFlow methods 
 
-### Basic interactive controls demo
+## Basic interactive controls demo
 * Demo on how to integrate basic interactive controls (slider bars, drop-down menus, check-boxes etc.) in a Jupyter notebook and use them for interactive machine learning task
 
 ## Run Jupyter using Docker