Machine Learning Fundamentals: A Concise Introduction

This book provides a clear and concise introduction to supervised machine learning, covering traditional and deep learning methods, with numerous case studies and examples. It assumes basic calculus, linear algebra, probability, and statistics, and is suitable for beginners with no prior exposure to machine learning.

\n Format: Paperback / softback
\n Length: 418 pages
\n Publication date: 25 November 2021
\n Publisher: Cambridge University Press
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This comprehensive and user-friendly guide to supervised machine learning offers a clear and concise introduction to core concepts for beginners. While the author assumes a basic understanding of calculus, linear algebra, probability, and statistics, no prior exposure to machine learning is required. The book covers a wide range of traditional and popular machine learning methods, including Support Vector Machines (SVMs), Boosted Trees, Hidden Markov Models (HMMs), and Linear Discriminant Analysis (LDA). It also delves into deep learning techniques such as Convolutional Neural Networks (CNNs), Attention Mechanisms, Transformers, and Generative Adversarial Networks (GANs).

Organized in a coherent presentation framework, the text begins by providing a foundational overview of machine learning principles and techniques. It then introduces each method step-by-step, starting from scratch and building upon the fundamentals. Clear and concise explanations are accompanied by detailed examples and case studies, demonstrating how the methods can be applied in real-world scenarios.

Throughout the book, a consistent style is employed to describe all methods and algorithms, ensuring a clear understanding for readers. The focus is on providing just the right amount of detail, without overwhelming the reader with unnecessary complexity. This makes the book accessible to a wide range of learners, from those with a technical background to those who are new to the field.

In addition to its instructional value, the book includes a comprehensive reference section that covers advanced topics and provides further insights into machine learning algorithms and their applications. Whether you are a student, researcher, or professional looking to expand your knowledge of supervised machine learning, this guide is an invaluable resource.

In summary, this introduction to supervised machine learning is a valuable resource for beginners and professionals alike. It offers a clear and concise explanation of core concepts, provides practical examples, and includes a comprehensive reference section. With its focus on accessibility and clarity, the book is an excellent tool for anyone seeking to gain a deeper understanding of this exciting field.

\n Weight: 908g\n
Dimension: 204 x 255 x 27 (mm)\n
ISBN-13: 9781108940023\n
Edition number: New ed\n