Machine Learning Algorithms and implementation provides an accessible overview of the field of machine learning, its applications, and implementation of algorithms. This Book presents some of the most important supervised and unsupervised techniques, along with implementation in python. Topics include Linear Regression, K-Nearest Neighbours, Naïve Bayes, Decision Tress, Random Forest, K-Means clustering. The goal of this textbook is to facilitate the use of these machine learning techniques by practitioners in science, industry, and other fields. Each algorithm contains a tutorial with example and its python implementation.