Top 10 Books on Artificial Intelligence for Beginners - Great Learning
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Top 10 Books on Artificial Intelligence for Beginners

Reading Time: 4 minutes

Top 10 Books on Artificial Intelligence for Beginners

Artificial Intelligence (AI) has taken the world by storm. Almost every industry across the globe is incorporating AI for a variety of applications and use cases. Some of its wide range of applications includes process automation, predictive analysis, fraud detection, improving customer experience, etc.

AI is being foreseen as the future of technological and economic development. As a result, the career opportunities for AI engineers and programmers are bound to drastically increase in the next few years. If you are a person who has no prior knowledge about AI but is very much interested to learn and start a career in this field, the following ten books will be quite helpful:

  1. Artificial Intelligence – A Modern Approach (3rd Edition)

By Stuart Russell & Peter Norvig

This book has been considered by many as one of the best AI books for beginners. It is less technical and gives an overview of the various topics revolving around AI. The writing is simple and all concepts and explanations can be easily understood by the reader.

The concepts covered include subjects such as search algorithms, game theory, multi-agent systems, statistical natural language processing, local search planning methods, etc. The book also touches upon advanced AI topics without going in-depth. Overall, it’s a must-have book for any individual who would like to learn about AI.

  1. Machine Learning for Dummies

By John Paul Mueller and Luca Massaron

Machine Learning for Dummies provides an entry point for anyone looking to get a foothold on machine learning. It covers all the basic concepts and theories of machine learning and how they apply to the real world. It introduces a little coding in Python and R to tech machines to perform data analysis and pattern-oriented tasks.

From small tasks and patterns, the readers can extrapolate the usefulness of machine learning through internet ads, web searches, fraud detection, and so on. Authored by two data science experts, this book makes it easy for any layman to understand and implement machine learning seamlessly.

  1. Make Your Own Neural Network

By Tariq Rashid

This book provides its readers with a step-by-step journey through the mathematics of neural networks. It starts with very simple ideas and gradually builds up an understanding of how neural networks work. Using Python language, it encourages its readers to build their own neural networks.

The book is divided into three parts. The first part deals with the various mathematical ideas underlying the neural networks. Part 2 is practical where readers are taught Python and are encouraged to create their own neural networks. The third part gives a peek into the mysterious mind of a neural network. It also guides the reader to get the codes working on a Raspberry Pi.

  1. Machine Learning: The New AI

By Ethem Alpaydin

Machine Learning: The New AI gives a concise overview of machine learning. It describes its evolution, explains important learning algorithms, and presents example applications. It explains how digital technology has advanced from number-crunching machines to mobile devices, putting today’s machine learning boom in context.

The book gives examples of how machine learning is being used in our day-to-day lives and how it has infiltrated our daily existence. It also discusses about the future of machine learning and the ethical and legal implications for data privacy and security. Any reader with a non-Computer Science background will find this book interesting and easy to understand.

  1. Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies

By John D. Kelleher, Brian Mac Namee, Aoife D’Arcy

This book covers all the fundamentals of machine learning along with practical applications, working examples, and case studies. It gives detailed descriptions of important machine learning approaches used in predictive analytics.

Four main approaches are explained in very simple terms without using many technical jargons. Each approach is described by using algorithms and mathematical models illustrated by detailed worked examples. The book is suitable for those who have a basic background in computer science, engineering, mathematics or statistics.

  1. The Hundred-Page Machine Learning Book

By Andriy Burkov

Andriy Burkov’s “The Hundred-Page Machine Learning Book” is regarded by many industry experts as the best book on machine learning. For newcomers, it gives a thorough introduction to the fundamentals of machine learning. For experienced professionals, it gives practical recommendations from the author’s rich experience in the field of AI.

The book covers all major approaches to machine learning. They range from classical linear and logistic regression to modern support vector machines, boosting, deep learning, and random forests. This book is perfect for those beginners who want to get familiar with the mathematics behind machine learning algorithms.

  1. Artificial Intelligence for Humans

By Jeff Heaton

This book helps its readers get an overview and understanding of AI algorithms. It is meant to teach AI for those who don’t have an extensive mathematical background. The readers need to have only a basic knowledge of computer programming and college algebra.

Fundamental AI algorithms such as linear regression, clustering, dimensionality, and distance metrics are covered in depth. The algorithms are explained using numeric calculations which the readers can perform themselves and through interesting examples and use cases.

  1. Machine Learning for Beginners

By Chris Sebastian

As per its title, Machine Learning for Beginners is meant for absolute beginners. It traces the history of the early days of machine learning to what it has become today. It describes how big data is important for machine learning and how programmers use it to develop learning algorithms. Concepts such as AI, neural networks, swarm intelligence, etc. are explained in detail.

The book provides simple examples for the reader to understand the complex math and probability statistics underlying machine learning. It also provides real-world scenarios of how machine learning algorithms are making our lives better.

  1. Artificial Intelligence: The Basics

By Kevin Warwick

This book provides a basic overview of different AI aspects and the various methods of implementing them. It explores the history of AI, its present, and where it will be in the future. The book has interesting depictions of modern AI technology and robotics. It also gives recommendations for other books that have more details about a particular concept.

The book is a quick read for anyone interested in AI. It explores issues at the heart of the subject and provides an illuminating experience for the reader.

  1. Machine Learning for Absolute Beginners: A Plain English Introduction

By Oliver Theobald

This book explains the various theoretical and practical aspects of machine learning techniques in a very simple manner. It makes use of plain English to prevent beginners from being overwhelmed by technical jargons. It has clear and accessible explanations with visual examples for the various algorithms.

To put machine learning in context, some basic Python programming is also introduced. The reader doesn’t need to have any mathematical background or coding experience to understand this book.

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