10 Best Machine Learning Books You Need to Know in 2020

By | February 19, 2020

What is machine learning? Machine learning is one of the hottest topics of 2020. It is essentially a branch of AI (Artificial Intelligence) that provides any machine the ability to make decisions without any human intervention or explicit programming. Machine learning algorithms are built by collecting huge amounts of data from various sources and building an intelligent system (model) based on past experiences, patterns and similar behaviors.

Machine learning applications

Machine learning has found applications in various fields with the most common being virtual personal assistants like Alexa, Siri, etc… Recommendation engines like those of Netflix (movies) or Amazon and Flipkart (online shopping) are also one of the most popular applications. Other than this, live traffic predictions, face detection, spam filtering, fraud detection, and digital medical assistance are some of the most powerful applications of machine learning.

Best Machine learning Books

Books are a great way to start your machine learning journey but don’t be overwhelmed at the list of books you will find when you do an online or offline search. The right book can keep you hooked up to the topic and start your ML journey in a proper manner. We have carefully compiled a list of 10 machine learning books, that will not only help you with basics but also serve as a step by step guide to enable you for hands-on with different algorithms.

1. Hands-on machine learning with Scikit-learn, Keras, and TensorFlow

Hands on Machine Learning with Scikit

This book is a good start for those who have no background in machine learning, whatsoever. It starts with very basic concepts and explains a large number of techniques in detail. The author also adds nice bits of humor to keep you engaged throughout. He also keeps a perfect balance of theory and practical implementation which is done in a subtle way along with easy to understand tips and tricks. The book doesn’t go into teaching Python, but if you are already familiar with Python, this should be your good-to-go book for ML. The book focuses on both how and why Machine learning – which means you always know why you are following a particular approach when you are doing it. If you are serious about learning ML, go for this book – it is intense, practical and needs a lot of focus.

Some highlights of the book –

  • Starts from traditional ML algorithms and moves on to deep learning and neural network techniques
  • A good balance of theoretical concepts and hands-on learning
  • All the example code given in the book are present in GitHub (so accessible anytime)
  • The book extensively uses frameworks like Scikit learn (first part of the book), TensorFlow and Keras (second part) that are used to solve real-life situations.
  • You should be knowing some basics about the basic Python libraries Numpy, pandas, and matplotlib, which by the way are super easy to learn.
  • The book also points to many other useful resources like online courses, other books, and data sources to continue your ML journey.

You can buy here.

2. Learning for absolute beginners

Machine learning for Absolute Beginners

As the name goes, the book teaches ML from “scratch”. It is a small book that can serve as a good starting point. The author knows what could be the pain points of ML and explains those carefully and in the right place. If you are looking for in-depth knowledge on all the ML topics, this book alone is not sufficient, although it does name all the topics you should be familiar with, so you can look them up and read from other resources.

Some highlights –

  • The book also covers little introduction about Python (in the Appendix), however, it will be good to learn the language (or know at least one programming language) before you start this book
  • The author illustrates a lot of concepts with simple diagrams that you can relate to – leading to faster and easier understanding
  • He focuses on a strong understanding of statistics and also briefs about the challenges faced in ML
  • Though at a high-level, the book covers all ML techniques starting from regression analysis to decision trees and artificial neural networks
  • Points to the right sources to download datasets for your own practice
  • If you do not understand ML terms and are totally new to the field – this book is the best place to start!

You can buy here.

3. Machine learning: An applied mathematics introduction

Machine Learning An Applied Mathematics

This is an intuition-based approach to machine learning that doesn’t overload you with technical details. It is a likable different approach which you will enjoy and comprehend easily. ML is a vast subject and needs a lot of time and effort, however, the author makes it more interesting and relatable due to this unique approach. The book covers all the ML topics from basics to advanced in detail. There are also many case studies and real-life examples from common domains like finance, politics, business, gambling, etc…

Some highlights –

  • Although the book covers a lot of topics in detail, it is still good for beginners to intermediate level learners.
  • The author maintains a friendly and witty tone throughout the book which makes the book quite interactive
  • If you are not yet fully convinced about learning ML and building a career in Data Science, this book will completely convince you into the field
  • The book doesn’t just talk about how ML algorithms work, it also brings a bigger perspective – the challenges, the problems associated with ML as of today.
  • The author neatly compares classical mathematical modeling techniques with ML techniques for us to get a good overview of what we are getting into.
  • There is no actual code – you would only find a description of what has to be done – so you can think of the code on your own!

You can buy here.

4. Deep learning (Adaptive computation and machine learning series)

Deep Learning(Adaptive)

If you are new into the field of data science and ML, this may not be the book for you – yet. The book is fairly advanced in its concepts and assumes some basic background from the reader. It focuses more on the concepts and covers them in-depth. The author starts with basic topics like scalars, vectors, linear algebra and goes on to advanced topics including deep learning research. The power of this book lies in the last few chapters – which are exhaustive, intense and focus on core deep learning methodologies. Keywords and important terms are explained well with easy to understand examples.

Some highlights –

  • If you are really interested in deep learning techniques, why, what and how of it, and the math behind each, this book will be your cherished treasure
  • The initial few chapters are dedicated to basic applied math concepts – linear algebra, probability, multiplication of vectors, determinant, etc… which set a strong foundation as well as the expectation for the rest of the book.
  • The final part of the book is dedicated to deep learning research which you will not find in any other book or material
  • There are plenty of resources and supplementary materials available on the website (deeplearningbook.org) that complements the book.
  • The book is different from other usual books that you will find on machine learning – it follows a more research and learning-based approach than implementation.

You can buy here.

5. Pattern recognition and machine learning

Pattern Recognition

This book straightaway gets into ML concepts with a simple example rather than giving a general introduction or background. I like the clear-cut and to the point approach of the author while explaining each topic and choosing the sequence and flow. There are a lot of graphical illustrations and formulae completely covering the mathematical aspects of probability, decision theory, information theory and distribution other than the ML techniques. Each chapter is followed by exercises that will make you think (you can also refer to the cross-references mentioned in the chapter).

Some highlights of the book –

  • The book dedicates separate chapters for each technique and thoroughly explains the models with simple relatable examples
  • Some parts of the introduction (like concepts of the matrix, polynomial distribution, etc) are explained well in the appendix section, so you can refer back and forth while reading those chapters. The data sets used in the chapters for illustration are also explained in the appendix (data itself can be taken from the book’s website) for practice and self-learning.
  • If you are not from a math background, it is better to read a simpler mathematics book on linear algebra, matrices, calculus, probability, and statistics. This book doesn’t cover much of the basics, it only covers these topics as required from ML perspective.
  • The book doesn’t bore you with too much theory, there are practical illustrations and graphs everywhere – you can easily get engrossed into it
  • It is a great self-learning book for intermediate learners.

You can buy here.

6. Python Machine Learning – Second Edition

Python Machine Learning

Python machine learning is an intermediate level book. You can still buy it as your first read if you are up for the challenge of supplementing your reading with other online and offline materials and do your own research along. Believe me, you will learn faster and better. This is one ideal book for software engineers and business analysts alike. It gives you a good working knowledge of all the concepts that you will need to build your career in data science – it has the perfect balance of theory and implementation.

Some highlights of the book are –

  • There are tips and notes throughout the book as if the author already knows what are the points you are going to get stuck while solving a problem!
  • If you are not from a coding background, you can still get most of the book because of the explanations that follow the examples.
  • Each of the concepts like classification, regression, linear algebra etc are explained with simple examples and beautiful illustrations, making it relatable and easy to understand.
  • The book also briefs about the important packages of Python and setting up the environment for further learning
  • The book is organized well. Each chapter naturally leads to the next and the level of complexity increases gradually.

You can buy here.

7. Machine learning for dummies

Machine Learning Dummies

Well, how can any list be complete without including the “for dummies” series of books? And why not? The author assumes you have no background of ML and explains thoroughly the basic concepts of ML taking common day-to-day scenarios as examples. The biggest advantage of this book is that you can get familiarized with both Python and R. The author’s style is mesmerizing and funny (as usual).

Some highlights –

  • The book starts with very simple and basic topics and as you move on from chapter to chapter, the complexity goes up.
  • It is a complete guide that covers every aspect of machine learning from what it is, why it is useful, history and background, building models, optimizing models, learning the most important packages, libraries and solutions to real-life problems.
  • There are lots of tips and additional resources scattered across the book that are helpful and will enhance your learning at every stage. These can be online resources like cheat-sheets or the code chunks present in the book for you to copy and paste (however, I would recommend you type at least a few lines of code on your own so that you get the knack of it).
  • It is a good book for trying on new things – experimenting and exploring machine learning on your own. The book boosts your imagination and motivates you to think differently.

You can buy here.

8. Fundamentals of Machine Learning for Predictive Data Analytics

Fundamentals of Machine Learning

This is an amazing book, which is self-contained and practical. The book not only explains how each ML algorithm works but also explains about strengths and weaknesses, variations and trade-offs for each. The concepts are beautifully explained without limiting the explanations to any particular programming language. The book is good for programmers as well as non-technical analysts.

Highlights –

  • The author builds a strong foundation of ML algorithms in a simple and engaging manner.
  • Each stage of data analysis including preparation applied ML and analysis are explained in-depth along with practical examples.
  • Very less of theory and more application-based approach
  • This book is a great starting point to build a strong foundation for your ML career.
  • Case studies provide an excellent insight into all the stages of ML and data science.
  • Even the smallest of concepts are explained in detail, a lot of other books miss to elaborate on such topics, like the metric system, binning, normalization, etc…

You can buy here.

9. Deep Learning with Python

Fundamentals of Machine Learning

This book is slightly different in its content and structure. The book talks about machine learning principles and techniques (Part 1) and then focuses on the deep learning techniques, advantages and challenges faced (Part 2). Practicing this book requires you to have prior experience in Python. However, it is not too technical, which means anyone can use this as a guide to learn about deep learning.

Highlights –

  • A good book for beginners to learn both ML and its subfield deep learning, the book’s approach is from simple concepts to more advanced and complex topics
  • Well organized and structured – it is like learning ML concepts in a guided classroom course.
  • Lots of practical examples that are relatable and easy to understand. The entire source code used is available on Github.
  • The book focuses on using Keras library, which is an additional plus point (Keras is one of the most popular and powerful libraries for practical purposes)
  • The author spends a good amount of time explaining basic concepts. As you practice along, the time to understand gradually reduces because the concepts are ingrained in your mind – learning becomes rather easy as the complexity of topics increases!
  • The author also adds his point of view on different topics of deep learning – which becomes helpful to have deeper and more intuitive insights.

You can buy here.

10. Machine Learning: A Probabilistic Perspective

Machine Learning a Probabilistic Perspective

Well, I somehow kept the best for the last. Not really, though. This is a great book – but not self-contained. It is somewhat advanced in nature and sometimes goes beyond comprehension for absolute beginners. However, the book is quite updated and has lots of things that you wouldn’t want to miss – great visuals and illustrations, loads of web-references, good examples. If you have a solid math background, you will enjoy this book thoroughly.

Highlights –

  • The book gives in-depth knowledge about modeling and evaluation, you think of a doubt, you will surely find the answer in the book.
  • It is a good book to get a detailed and strong foundation of all the statistics and math-related to ML – so much so that you might want to train others as well.
  • Focuses more on Bayesian statistics and strongly supports Bayesian theory
  • Some steps are left out for the readers to figure out – you have to be at least an intermediate level learner to do that
  • The book is much detailed not just about the techniques, but also the idea behind them and compares different techniques so that the reader can use their best judgment to use the right technique for the right problem.
  • Overall a good book for both students, teachers, and practitioners of ML

You can buy here.


There are a lot more books on machine learning, but what I have listed above is an essential set. It does not mean you have to immediately get all of them and also doesn’t mean that just one or two of them are sufficient. As the field is ever-evolving, our knowledge has to grow for us to be more intuitive and informative both. Some basic books are self-contained, for example, machine learning for absolute beginners and machine learning for dummies, but may not be enough if you are looking for deeper explanations. In that case, you might want to complement these with Hands-on machine learning with Scikit-learn, Keras, and TensorFlow or Pattern recognition and machine learning.

Same way, the book Deep Learning with Python is a great start, but it would be nicer to read this book along with Deep Learning (Adaptive Computation and Machine Learning series) or Python machine learning – Second edition.

You can choose to buy whatever combination of books you want to, based on the information given in this article. I would recommend you start with Machine learning for absolute beginners, followed by any other intermediate or another basic level book if you have no idea about ML or Python. If you know math or Python or a little bit of ML, ML for dummies or even Fundamentals of Machine Learning for Predictive Data Analytics would be a good choice to begin.

Happy learning!

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