Module 1 - Introduction to Data Science and Machine Learning - Introduction to Python, Data Structures of Python, NumPy and Pandas Libraries of Python, Applications of Machine Learning, Supervised vs Unsupervised Learning, Python libraries suitable for Machine Learning, Plotting graphs and trends using Python, Math for Machine Learning- Linear Algebra, Matrices, Calculus and Vectors.

Module 2 - Regression - Linear Regression, Linear Regression Assessment, Non-linear Regression, Model evaluation methods, Advanced Regression.

Module 3 - Classification - Introduction to Classification problems, K-Nearest Neighbour, Decision Trees, Logistic Regression, Support Vector Machines, Model Evaluation- Bootstrapping and majority rule.

Module 4 - Unsupervised Learning - K-Means Clustering, Hierarchical Clustering, Density-Based Clustering, Evaluation of clustering algorithms, Principal Component Analysis, Feature Selection.

Module 5 - Supervised Learning - Deep Learning, Artificial Neural Network,Convolutional Neural Network, Recurrent Neural Networks , Understanding backpropagation, Collaborative Filtering,Reinforcement Learning.