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Best Books on Machine Learning

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As one of the hottest domains of computer science, machine learning certainly grabs the attention of many. If you are someone who wants to learn the language, we have some excellent book recommendations for you. 

In this list, you will find some of our top picks to understand the subject of machine learning. So go ahead and browse the best one out of the lot. 

7 Best Books to Learn Machine Learning & Reviews

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

Author: Oliver Theobald

Publisher: Independently published 

Pages: 155

A subject such as machine learning should not be taken lightly. So, here is a book that gets you started with the theoretical and practical principles at first. Hence, this book helps you get started with a comfortable stance before taking you to the more complex topics. 

The author makes a practical entry into the field of machine learning with this book. Yet, amazingly he manages to make it a high level introduction that the readers will be thrilled to read. With this book, there is a lot to learn and understand in the field of machine learning. The data enlisted in the book is of high value to the readers spanning multiple topics. 

The author does not lose sight of his ultimate target to educate the readers. Using a comprehensive set of facts and knowledge, he achieves total discovery of this scientific field. 

Summary: The readers will find this book useful to get a quick overview of topics. However, the lack of expansion might put the reader in need of further explanations on various topics and data. 

2. Machine Learning For Dummies 

Author: John Paul Mueller and Luca Massaron 

Publisher: For Dummies

Pages: 432

The book unzips the concepts and fundamentals of machine learning down to their very core. As the name suggests, this book is a friendly entry point for beginners in the field of machine learning. The book throws an essential light over pivotal elements of the tech world, where machine language proves to be a boon. 

The author comfortably scraps the dust over the known capabilities of machine learning. So in doing this, he highlights the practical applications of machine learning and how it helps the world. 

The author makes immense sense with his plain and simple language and successfully explains the concepts with clarity. Reading this book will help the users to grab the essence of machine learning. It is perhaps even helpful for them to implement the knowledge of the field in real time. 

Summary: Readers who are absolute beginners in the field of machine learning can make good use of the book. It will be appreciated for its inclusion of topics and wide scale coverage across the topics. 

3. Machine Learning: The New AI (The MIT Press Essential Knowledge Series)

Author: Ethem Alpaydin

Publisher: The MIT Press 

Pages: 224

This book is proficient in presenting a bird’s eye view of various machine learning concepts. The book explores the zenith of data processing and how it impacts human life. The author makes a strong attempt to pen down this book which is no less than a collection of useful information.

The author scales the advancement of the machine language with accuracy and examines its evolution in the present day context. This book is highly beneficial for someone who is beginning to explore the subject and is looking for in-depth information on the same. 

The book also includes several new additions which provide a gateway to complete understanding of the subject. There are many topics in the book which drive a logical and comprehensive coverage of machine learning. For a reader, this book can prove to be very effective and efficient at learning. 

Summary: The readers will appreciate the wide scale overview of the concepts. However, the book remains aloof from in-depth explanations which can be bothersome for some readers, who otherwise wish to get a detailed understanding. 

4. Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (The MIT Press)

Author: John D. Kelleher and Brian Mac Namee

Publisher: The MIT Press

Pages: 624

This book is entirely self sufficient at describing the context of machine learning. It elaborates on many aspects of the field. So, from an understanding perspective, it has much to offer to the readers. The author has made some interesting discussions in this book on the subject matter. Thus, an in-depth read will reveal a breakthrough perspective in machine learning to the reader. 

It will also not be wrong to say that the book is highly optimised to suit the learning needs of the reader. There is a clear emphasis on a problem and solution situation so that the learner can gain a wider perspective to untangle the problems in this field. 

Further, the author also makes use of various illustrations and case studies to make the concepts clearer to the readers. Thus, this book emerges as a useful learning resource for many students and readers. 

Summary: With a clear and transparent writing style, the author achieves the target to attract the reader’s eye. This book can be found effective for intermediate users who have the basic knowledge of machine learning and wish to progress further. 

5. Machine Learning: The Art and Science of Algorithms that Make Sense of Data

Author: Peter Flach

Publisher: Cambridge University Press

Pages: 409

Among the most remarkable features of the book is that it is clearly well organised and well researched. Hence, it makes sense why it remains useful to a large number of readers. The author guides the reader through the purpose and process of machine learning. 

In doing so, he elucidates a number of facts and insights, which prove to be of much value to the user. At the same time, the author does not fail to describe how machine learning techniques operate. He goes on to explain a number of algorithms associated with machine learning. Thus, this book provides an opportunity for the reader to gaze on a wide array of knowledge. 

Summary: The readers will appreciate the clear writing style of the author but the use of practical explanations will attract them the most and provide valuable learning. 

6. Programming Collective Intelligence: Building Smart Web 2.0 Applications

Author: Toby Segaran 

Publisher: O’Reilly Media

Pages: 360

This book is practical insight into the world of machine learning. The author has cultivated this book with the richness of data, facts and numbers. Thus, he is able to demonstrate a number of findings and facts, based on their support. 

The author shares extensive knowledge, experience and learnings in this field through this book. The author elucidates upon a number of concepts and topics in this book, driving seamless understanding. Thus, the information included herein is based on the practical use of the power of machine learning in the modern era.

The book is also rich in its coverage of concepts and topics. So, a reader can rely on this book for the overall study and quick understanding of machine learning. To make things easier for the readers, the chapters in the book are logically subdivided. At the same time, the entire work of the author is well supported by the use of algorithms. 

Summary: This is a mid level book that can be read and understood by someone who has some basic understanding in the field of machine learning because there is no dearth of explanations or conceptual coverage in this book. 

7. Pattern Recognition and Machine Learning

Author: Christopher M. Bishop

Publisher: Springer

Pages: 738 

The wealthiest range on our list, this book is a treasure of knowledge because it has been designed with the intent to assist a wide array of users. This includes students, teachers and instructors as well. The author has compiled this book and its content after conducting much research on the matter and detailing the matter with relevant facts and figures. 

As a reference book, this title does immense justice to the needs of the students and the readers will find that the book is clearly well structured. So there is no need for them to spend their time locating any particular information. 

Also, the book is rich in practice exercises for the students who can test their understanding with their help. The language used by the author is plain but authoritative. Thus, there is no hurdle to understanding the contents of the book with ease. 

Summary: It is commendable to see how the book is accessible to a wide array of users because there is no lack of illustrations in the book and the inclusion of exercises at the end of each chapter is highly useful. 

In Conclusion

These are among the best books on machine learning that we have gathered for you. We hope that you will find this list complete and useful for your purpose. These books are relevant for students as well as for general learners who wish to know more about machine learning. If you have read another book on machine learning, do share it with us as well. a

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