Machine Learning: Theory to Applications

This book aims to teach core concepts of machine learning while focuses on modern applications. It is aimed at those who want to master machine learning by providing technical and practical insights. It avoids using complicated mathematics to explain how to use the benefits of machine learning algorithms in what you do. Unlike the existing books, this work provides the core concepts with emphasis on fresh ideas and real application scenarios. It starts with the basic concepts of machine learning and extends to the different deep learning algorithms. The book provides the introduction and main elements of evaluation tools with Python and walks you through the recent applications of machine learning on self-driving cars, cognitive decision making, communication networks, security and signal processing. The concept of generative networks is also presented and focuses on GANs as a tool to improve the performance of existing algorithms.

In summary, this book offers a comprehensive technological path from fundamental theories to the categorization of existing algorithms, covers state-of-the-art, practical evaluation tools and methods to empower you to use synthetic data to improve the performance of applications.