"Machine Learning is a cutting-edge branch of Artificial Intelligence that has brought forth exciting new technological advances in recent years. This book introduces this important topic of current interest while also explaining its practical applications. Aimed at graduate students, teachers and researchers, this book will also help practitioners in implementing ML algorithms.This book explores concepts such as feature engineering, model selection, model estimation, model validation and model explanation and provides an in-depth discussion of the main classification and clustering techniques and algorithms. It also examines optimal predictors and provides an introduction to Deep Learning architecture, including autoencoders and various neural networks.This book is a valuable resource for anyone interested in machine learning, data mining and pattern recognition.Salient features•Clear and concise chapter learning objectives and summary of topics•Over 125 solved examples to aid and enhance understanding of concepts•Over 150 figures to provide visual impact and envisage abstract concepts•Applications drawn from real-life data sets•Over 125 conceptual and application-based exercise questions•Comprehensive bibliography of sources and topics for further reading•Appendix with hints in the form of code snippets for all the practical exercises•Android app with chapter-wise PowerPoint slides and code snippets for the ML programs given in the bookOnline resources available at: https://www.universitiespress.com/MachineLearningTheoryandPractice"