7 Best Machine Learning Books of 2022 (Ranks and Reviews)

best machine learning books
Machine Learning (Image via Becoming Human)

Want to get right to the best machine learning books right now? My favorite choices are Hands-on Machine Learning and Machine Learning Design Patterns.

Machine Learning is one of the most exciting, evolving, and most in-demand skills today. There is no doubt that almost every organization all over the world today is making the most of this innovative technology to make smarter business decisions.

But, it is an extremely broad and complex world. With the emergence of Deep Learning, machine learning has grabbed more attention in recent years.

In fact, according to a report from information-age.com (published in 2020), tech giants like Google have spent more than $4 billion acquiring ai startups, Apple has spent more than $886 million acquiring ai startups, and Facebook as well has spent more than $1 billion acquiring ai startups – all since 2009.

And, according to a bcc research, the global machine learning market totaled $1.4 billion in 2017 is estimated to reach $8.8 billion by 2022 growing at a compound annual growth rate (CAGR) of 43.6% for the period of 2017-2022.

That’s a huge tech market i.e. worth exploring.

Artificial Intelligence is here to stay, with more and more developers jumping on the machine learning bandwagon. And, there’s no better way to get started other than reading some of the best machine learning books – for tech lovers.

So, you want to learn, research, and explore the amazing tech world.

Welcome to the club of tech enthusiasts.

In today’s list, I set out to find the best machine learning books of the year to make it easier for you to learn about this exciting yet complex concept of ML.

Disclaimer: I may earn affiliate commissions from Amazon LLC if you decide to purchase any book on amazon through checkout links available on this page. However, these commissions are at no extra cost to you and my goal is to give you the very best recommendations. Read more about it here.

Let’s get started.

What is Machine Learning (ML)?

Machine Learning is a subset of Artificial Intelligence (AI). It is the science of getting computers to act without being explicitly programmed.

In other words, machine learning is any technique that gives computers the ability to learn from data, identify patterns and make predictions with little or no human intervention.

Qualitatively, machine learning is the ability to derive patterns from data and use those patterns to make predictions or decisions; the aim of machine learning is to develop computer programs that can learn from data.

With the increasing power of computers and advancement in artificial intelligence, machine learning is a perfect reprieve for all those who loathe manual tasks – whether it involves summarising text or spotting complex patterns.

Which is the Best Book for Machine Learning?

Here are my top picks for the best machine learning books to read this year:

1. Hands-On Machine Learning.

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow demystifies machine learning and shows you how to use it in your projects.

Excluding all other things, you’ll learn the three key components of this technology: artificial neural networks aka deep learning, support vector machines, and reinforcement learning.

Author Aurélien Géron teaches you the fundamentals with illustrations and sample code for a wide variety of learning tasks, showing how to train everything from a simple linear regression model to a deep neural network — and even a recurrent neural network.

The book begins with a gentle introduction to the key concepts of machine learning and then progresses by adding new layers of complexity throughout the rest of the book.

You’ll learn about machine learning models, prediction methods, and recommendation systems, and apply these concepts in projects involving computer vision, social network analysis, and text analysis.

In the end, you should have a solid grasp of all the necessary components of a machine learning and deep learning system and how they work together.

Are you ready to put machine learning to work in your projects?

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2. Machine Learning Design Patterns.

Machine learning practitioners, data scientists, and software engineers face common challenges as they work to develop ML solutions: uncertainty, complexity, scale, and reproducibility.

Machine Learning Design Patterns seeks to illuminate common design patterns and practices they use at multiple levels. These patterns make it easier to represent data, build models, operate models in production, and explain their outcomes based on the needs of their organizations.

Patterns are concise, general statements that capture the essence of a commonly occurring solution to a recurring problem in a given context. By capturing solutions to recurring problems and bundling them within a given domain or context, patterns allow for efficient reuse in different situations.

Often, the design of an ML system is influenced by the choice of pattern(s) used. The advantage of this approach is that if you have prior experience with similar systems, that knowledge can be leveraged across diverse domains and enable you to rapidly develop new solutions.

The book focuses on machine learning patterns. Patterns include solutions related to data preparation, model building, monitoring and managing ML systems, and deploying ML systems within production environments.

By following the learned design patterns in this book, you will be able to build machine learning systems more quickly and easily. The book starts by providing best practices for preparing data, building learning models, and carrying out operations over them.

At the end of this book, you will have a better understanding of the whole machine learning workflow in a way that can also be easily applied to your future projects.

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3. Pattern Recognition and Machine Learning.

The aim of this book is to present a conceptual and mathematical framework for machine learning problems under uncertainty.

The key feature of the book is the treatment of approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible.

In doing so, it incorporates graphical models to describe probability distributions, which allows the reader to understand exactly what is going on in the algorithm.

It also presents new concepts such as the use of MAP-MRF lower bounds to obtain practical efficient algorithms and proves basic consistency results allowing a clear understanding of convergence.

The algorithms make it possible to obtain approximate answers with minimal effort in these situations of intractability and discontinuity. These algorithms could help software engineers incorporate machine learning into their programs.

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4. The Hundred-Page Machine Learning Book.

Have you been looking for a practical, hands-on guide to quickly get started with machine learning (ML) and deep learning?

The Hundred-Page Machine Learning Book is a concise introduction to ML via practical recipes. The book contains 100 pages of content, plus an appendix with exercises, which is more than enough material to teach you how to apply ML in your day-to-day engineering work.

Written by a true practitioner of the subject, this book provides a crisp overview without getting into the depths of mathematical models that are required for advanced levels.

It is an intuitive approach that demystifies much of the mystery around ML— a must-have for beginners!

It is focused on practical tasks and helps you solve a range of problems by leveraging various machine learning algorithms.

It starts off with a general introduction to machine learning, introduces the fundamental concepts and terminologies behind it, especially TensorFlow, before diving into more advanced topics such as training and implementing neural networks, building recommendation systems, and solving reinforcement learning problems.

This book includes guidance on using TensorFlow to train networks of varying complexity as well as techniques to optimize training time and resources.

You will also discover the TensorFlow Probability API along with support for deep neural networks.

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5. Machine Learning: A Probabilistic Perspective.

The need for adapting computer programs to new data has always been crucial to the success of software development and this has become more pressing with the Web-enabled deluge of electronic data.

To address how software adapts to new circumstances, probabilistic models can be applied that represent knowledge about the data and use it for predictive modeling.

Probabilistic machine learning provides the engineering techniques needed to develop effective learning methods and automatically uncover hidden patterns from observed data.

The book explains these techniques in detail, building up a cumulative knowledge of probabilistic inference that unifies the field.

The text stresses conceptual understanding, provides an in-depth discussion of theoretical results, considers a wide range of examples and applications, illustrates how to design effective learning algorithms using probabilistic models, and also considers recent important advances in the field.

This authoritative book begins by defining the field of machine learning and reviewing the essential probabilistic models and statistical inference techniques used in most machine-learning methods.

The book then focuses on probabilistic graphical models, which provide a flexible way to represent probability distributions for modeling complex relationships among variables. It presents algorithms for inferring the structure of such graphical models from data.

The text presents a variety of contemporary, real-world applications to illustrate how machine learning is used in practice, including speech recognition, computer vision, natural language processing, bioinformatics, and robotic control.

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6. Data Mining.

Providing both a broad perspective on the field of data mining and practical advice, this book will help you apply machine learning to your next project.

Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition presents the basic and advanced principles of data mining, while presenting coverage of all the important topics needed in order to apply these methods to a real-world situation.

Based on groundbreaking work that brought neural networks to industry and academia, this book is ideal for data miners at all career levels and provides numerous examples of how to put machine-learning theories into practice.

This book offers a comprehensive view of data mining, equipping students and practitioners with a clear sense of the processes involved in data mining, as well as guidance on applying machine learning algorithms and tools to build predictive models.

The author begins with a conceptual overview of data, data types, and exploratory data analysis.

The book then covers classification, regression, association rules, clustering, dimensionality reduction techniques, and model evaluation methods. Exercises throughout the text help readers apply their new knowledge to real-world situations.

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7. Machine Learning Engineering.

Ever since tech giants like Google and Amazon started investing in artificial intelligence startups, AI has become a hot topic in technology. 

Any discussion about developing new or improving existing software systems seems incomplete without mentioning the possibilities that modern programming languages, open-source libraries, and cloud services offer in their respective domains. 

The problem is, most engineers and IT professionals have little to no experience in the field and are not well trained. This means that those who are diving into the pool for the first time need a guide to help get started. 

That’s where Andriy Burkov’s latest book comes in.

Andriy Burkov does a wonderful job of communicating the importance of both business and technical understanding by combining them in a practical way that allows us to learn from real-world examples. 

The approach Andriy takes is great for beginners because he is able to cover some very advanced and technical topics with the proper context and explanation. 

This book does a terrific job of presenting this information in a way that walks you through a lot of the concepts but then leaves plenty to dig into on your own – so it’s not just one of those books meant to be consumed at once shelved.

Although machine learning is a science that’s been around for a long time (by some measures, it’s nearly 100 years old), it’s only in recent years that the technology has become mature enough to begin seeing real adoption. 

That’s why I believe we’re at an exciting crossroads today with machine learning — it’s still early enough that there are plenty of opportunities to lead, but mature enough that we can start seeing real results. 

Understanding key concepts and techniques are crucial, but ultimately the best way to learn is by doing, so follow Andriy Burkov’s advice and get started right away!

Machine Learning Engineering is a great book for the engineering audience of data scientists and machine learning practitioners (and future Data Scientists) who want to build scalable machine learning solutions in the real world. I am extremely impressed with this book. 

The book is well written, provides quality diagrams and code examples, and contains current industry best practices of how to solve problems with big data and machine learning. 

Every chapter is full of best practices, design patterns, gotchas, and code examples that can be immediately applied to real-world projects.

I highly recommend it to serious tech enthusiasts!

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Summary

That’s it for my list of the best machine learning books.

Machine Learning has become one of the most popular concepts in technology companies and software development organizations. 

Driven by the demand for automation and reduction of human interaction in data processing, Machine Learning is emerging as a great way to automate tasks otherwise done manually. 

With recent advances in both hardware and software technologies, Machine Learning is moving from being used for research purposes to being used for commercial needs. That’s where this list will help you deal with the learning process of machine learning more easily. 

Here is a final summary of my top picks:

  1. Hands-On Machine Learning.
  2. Machine Learning Design Patterns.
  3. Pattern Recognition and Machine Learning.
  4. The Hundred-Page Machine Learning.
  5. Machine Learning: A Probabilistic Perspective.
  6. Data Mining.
  7. Machine Learning Engineering.

What did you think of this list? Are there any books not mentioned? Let me know in the comment section.

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