Top 5 All-time Favourite Machine Learning Books


Machine learning has given humanity the ability to conduct activities in an automated manner. It allows us to enhance things we already accomplish by studying a continuous stream of data about that activity. Machine learning has numerous uses, including space research and digital marketing, in a wide range of disciplines.

Artificial intelligence is also based on machine learning. We aren’t yet overwhelmed with computers that can make decisions on their own. It’s a long road ahead but opportunities opening up along the journey are limitless.

In this blog, we’ve listed the five must-read books on Machine Learning. These books are mentioned in no particular order. The objective is not to promote any book but to help create awareness of a world beyond podcasts and video tutorials.

Let’s get started!


Top 5 Machine Learning Books (Beginners)


  1. Machine Learning For Absolute Beginners- A Plain English Introduction (Second Edition) by Oliver Theobald

It is an excellent place to begin as it doesn’t require any prior experience or knowledge in machine learning or data science. It starts from the basics of programming and goes all the way to giving us insights on how we can use machine learning for prediction. The focus is primarily on statistics rather than computer science, but its hands-on approach makes it ideal for beginners. This book also has an accompanying website that provides extra resources that will help you learn about machine learning quickly and efficiently.

This Machine Learning for Beginners Book will guide you through the fundamentals of machine learning, from downloading free data to the tools and machine learning libraries you’ll need. The book begins by establishing a firm foundation in the basics of machine learning, including data preparation methods, regression analysis, clustering, and more. If you haven’t experienced the ‘Lion King’ moment where you proudly gaze over the vast expanse of ML-like Simba looks on the continent of Africa’s Pride Lands, this is the ideal book to lift you and provide you with a clear picture gently.


  1. Machine Learning in Python and R For Dummies (First Edition) by John Paul Mueller & Luca Massaron

This book is excellent if you are interested in the practical side of machine learning. It provides a good grounding for an AI novice and contains many real-world examples that can be used for machine learning. The explanations are very detailed and not too complicated. 

This book does not require any prior knowledge or experience in machine learning, even though it has been written to include details about certain mathematical concepts related to machine learning. But you will get to grips with maths pretty quickly by following the few simple rules suggested by the author at the start of this book. The code samples are available online, so you can download them without having to complete your read.


  1. Machine Learning for Hackers- Case Studies and Algorithms to Get You Started (First Edition) by Drew Conway & John Myles

If you are looking at Machine Learning for Hackers, then this book is a perfect choice. It’s not an extremely technical read, but it does go into enough depth to give you practical examples of various machine learning algorithms in action. Some data scientists even use this book as their training manual. We can also say that this is one of the most well-regarded books on the subject. 

Even though this book was initially intended for programmers, it still could be considered for beginners because high school maths isn’t required to understand its contents. The author has explained things very clearly and in detail, with each step illustrated in simple English which helps you in mastering the art.


  1. Machine Learning- The New AI (The MIT Press Essential Knowledge Series) by Ethem Alpaydin

The book offers an overview of artificial intelligence and machine learning, including the many business opportunities that they present. The author explains how machines can learn, providing examples of real-world applications in each case. With a focus on practical applications, this is a reliable choice if you want to stay away from highly technical books. This is also available as one of the free data science book PDFs that you can download from our website without going through any registration or payment process.


  1. An Introduction To Statistical Learning: with Applications in R (Use R) by Gareth James et al

The book offers a detailed account of different methods for regression and classification tasks and an examination of how these methods work together under the overarching theme of statistical learning. The book is ideal for performing regression or classification tasks, but it doesn’t stop there. The author also explains how to interpret the results of your analysis to make better predictions based on them.


Top 5 Books on Machine Learning (Experts/ Intermediates)

  1. Pattern Recognition & Machine Learning (First Edition) by Christopher M. Bishop

It is written for students of machine learning and people with a solid mathematical background. It provides an overview of both supervised and unsupervised learning methods, along with the mathematics behind these approaches. It is not just a maths book that happens to have ML examples in it; instead, all concepts are explained using real-world data as far as possible. After reading this book, you should understand basic techniques such as linear regression, decision trees, and boosting algorithms, which will help you become well-versed in the Machine Learning framework.


  1. Fundamentals of Machine Learning (For Predictive Data Analytics) by John D. Kelleher

The book is unique because it presents the mathematical concepts simply without assuming too much prior knowledge of maths or programming. The book has four sections- Foundations, Decision Theory, Regression and Classification, and Support Vector Machines. Each section comprises self-contained lectures that can be studied in any order. Even though this doesn’t have any coding example, the author still provides the best explanations for understanding ML algorithms while giving deep insight into statistical foundations, which are important while working on real-life projects.


  1. Machine Learning- The Art and Science of Algorithms that Make Sense of Data (First Edition) by Peter Flach

This is a highly accessible and comprehensive guide to machine learning based on the author’s years of experience. The book covers techniques such as deep learning, Bayesian methods, hidden Markov models and boosting, which help readers understand the best algorithms for their data. It also provides code examples in R throughout the book, covering all of the latest features that have been implemented in this programming language.


  1. Programming Collective Intelligence- Building Smart Web 2.0 Applications (First Edition) by Toby Segaran

The book mentions how to build applications that use a large amount of data available on the internet. You will learn about different machine learning, natural language processing and MapReduce algorithms and how they can be used to build real-world applications such as web search engines, automatic recommendation systems, online advertising systems, etc. Using a practical example, the author shows how a program can collect millions of documents from websites such as Twitter or Wikipedia to create an accurate picture of what’s going on in the world right now.


  1. Machine Learning- An Algorithmic Perspective Second Edition) by Stephen Marsland

This book has been written for readers with some background knowledge in computer science and mathematics who want to learn about machine learning. The author explains the concepts behind different algorithms and techniques such as boosting, support vector machines etc. He even covers unsupervised learning, an essential aspect of Machine Learning that is very useful for understanding data and gaining valuable insights.



That sums up the top five machine learning books for beginners and the top 5 for experts or intermediates or anyone who wishes to progress in the field of machine learning as they choose. Apart from reading books, you may learn about machine learning by watching the best machine learning tutorials, YouTube videos, online courses, and so on.

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