Includes bibliographical references and index.

"Machine learning is an application of artificial intelligence that focuses on the development of computer-based programs that can access data and use it to learn for themselves. In this book, we present the basics of machine learning including the four unsupervised, semi-supervised, self- supervised and reinforcement learning. In recent years, neural networks have appeared in many applications with deep learning concepts. In this book, we review the theory of different deep learning techniques including convolutional, recurrent and feed-forward neural networks. This book also provides the reader with a guided tour of needed tools and evaluation techniques in Python that helps the reader to understand the applications of machine learning techniques. The key feature of this book is its focus on recent applications of machine learning and deep learning techniques that benefit from new ideas including generative networks to pre-process the data set or to produce the synthetic data for reducing the actual data-set sizes or improving the performance. We also present the different models of generative adversarial networks and their advantages on applications such as image processing, new communication networks, cognitive science, security and signal processing"--