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Full Description
The main goal of the book is to provide a comprehensive and accessible guide that empowers readers to understand, apply, and leverage machine learning algorithms and techniques effectively in real-world scenarios.
Welcome to the exciting world of machine learning! In recent years, machine learning has rapidly transformed from a niche field within computer science to a fundamental technology shaping various aspects of our lives. Whether you realize it or not, machine learning algorithms are at work behind the scenes, powering recommendation systems, autonomous vehicles, virtual assistants, medical diagnostics, and much more. This book is designed to serve as your comprehensive guide to understanding the principles, algorithms, and applications of machine learning. Whether a student diving into this field for the first time, a seasoned professional looking to broaden your skillset, or an enthusiast eager to explore cutting-edge advancements, this book has something for you.
The primary goal of Machine Learning for Industrial Applications is to demystify machine learning and make it accessible to a wide audience. It provides a solid foundation in the fundamental concepts of machine learning, covering both the theoretical underpinnings and practical applications. Whether you're interested in supervised learning, unsupervised learning, reinforcement learning, or innovative techniques like deep learning, you'll find comprehensive coverage here. Throughout the book, a hands-on approach is emphasized. As the best way to learn machine learning is by doing, the book includes numerous examples, exercises, and real-world case studies to reinforce your understanding and practical skills.
Audience
The book will enjoy a wide readership as it will appeal to all researchers, students, and technology enthusiasts wanting a hands-on guide to the new advances in machine learning.
Contents
Preface xix
1 Overview of Machine Learning 1
2 Machine Learning Building Blocks 21
3 Multilayer Perceptron (in Neural Networks) 51
4 Kernel Machines 63
5 Linear and Rule-Based Models 73
6 Distance-Based Models 87
7 Model Ensembles 103
8 Binary and Beyond Binary Classification 117
9 Model Selection 127
10 Support Vector Machines 149
11 Clustering 177
12 Reinforcement Learning 205
13 Recommender Systems 225
14 Advancements in Deep Learning 245
15 Advanced Deep Learning Using Julia Programming 277
16 Machine Learning for Industrial Applications 297
Index 317
