Update README.md

Author: tirthajyoti

README.md

@@ -26,7 +26,7 @@ Jupyter notebooks covering a wide range of functions and operations on the topic
 
 * 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
 <img src="https://qph.fs.quoracdn.net/main-qimg-914b29e777e78b44b67246b66a4d6d71"/>
@@ -37,6 +37,8 @@ Jupyter notebooks covering a wide range of functions and operations on the topic
 <img src="https://docs.opencv.org/2.4/_images/optimal-hyperplane.png"/>
 * Naive Bayes classification
 
+---
+
 ### Clustering
 <img src="https://i.ytimg.com/vi/IJt62uaZR-M/maxresdefault.jpg" width="450" height="300"/>
 
@@ -47,14 +49,20 @@ Jupyter notebooks covering a wide range of functions and operations on the topic
 * Hierarchical clustering with Dendograms showing how to choose optimal number of clusters
 <img src="https://www.researchgate.net/profile/Carsten_Walther/publication/273456906/figure/fig3/AS:294866065084419@1447312956501/Example-of-hierarchical-clustering-clusters-are-consecutively-merged-with-the-most.png" width="700" height="400"/>
 
+---
+
 ### Dimensionality reduction
 * Principal component analysis
 
 <img src="https://i.ytimg.com/vi/QP43Iy-QQWY/maxresdefault.jpg" width="450" height="300"/>
 
+---
+
 ### Deep Learning/Neural Network
 * 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 
+* Step-by-step building of 1-hidden-layer and 2-hidden-layer dense network using basic TensorFlow methods
+
+---
 
 ## Random data generation using symbolic expressions
 * How to use [Sympy package](https://www.sympy.org/en/index.html) to generate random datasets using symbolic mathematical expressions. Here is my article on Medium on this topic: [Random regression and classification problem generation with symbolic expression](https://towardsdatascience.com/random-regression-and-classification-problem-generation-with-symbolic-expression-a4e190e37b8d)