Course Modules

Program Details

Day 1: Machine Learning in a Nutshell, Probability Distributions and Distance Functions, Regression Analysis (Linear and Logistic), Data Visualization, Regression Analysis.

Day 2: Introduction to Classification, Dimensionality Reduction (PCA, LDA, and likes), Implementing PCA and LDA, Feature Selection in Practice.

Day 3: Introduction to Clustering, Mixture Models and EM, Clustering in Practice, Implementing EM.

Day 4: SVM with Kernel Trick, Decision Tree and CART, Classification in Practice, Implementing CART.

Day 5: Ensemble Learning (Random Forest, Bagging, Boosting), Neural Networks (MLP, AE), Implementing RF, Implementing MLP.

Day 6: Deep Neural Networks, Bayesian Learning, Implementing CNN, Implementing RNN.

The detailed course is outlined here.