Topics to be covered
 Introduction to Machine Learning (ML)
 Regression: LASSO & Ridge Regression
 Kmean, KNN and Bayesian Classification
 Dimensinality Reduction: PCA and ICA
 Perceptron & Logistic Regression
 Support Vector Machine
 Artificial Neural Network
 Optimization for MLand DL and Evolutionary Algorithms
 Deep Learning (DL)
 Representation & Transfer Learning
 Autoencoders, CNN, RNN and GAN
 Handson using Google Collab.
 Use of ScikitLearn, Pytorch and Tensor Flow

