The book offers the readers the fundamental concepts of Machine Learning techniques in a user-friendly language. The book aims to give in-depth knowledge of the different Machine Learning (ML) algorithms and the practical implementation of the various ML approaches.This book covers different Supervised Machine Learning algorithms such as Linear Regression Model, Na?ve Bayes classifier Decision Tree, K-nearest neighbor, Logistic Regression, Support Vector Machine, Random forest algorithms, Unsupervised Machine Learning algorithms such as k-means clustering, Hierarchical Clustering, Probabilistic clustering, Association rule mining, Apriori Algorithm, f-p growth algorithm, Gaussian mixture model and Reinforcement Learning algorithm such as Markov Decision Process (MDP), Bellman equations, policy evaluation using Monte Carlo, Policy iteration and Value iteration, Q-Learning, State-Action-Reward-State-Action (SARSA).