There’s no single book that can help you fully master machine learning. It’s a complicated subject that spans many topics, purposes, and of course benefits in real-world applications.
But this post should help novices and experts alike find the right book to continue their education. With so many resources available it can be tough knowing where to start. I’ll consider 10 books and look at each one’s teaching style, subject matter, and recency of publication.
Absolute beginners will be looking for the #1 book to start with. I’m surprised to write this recommendation but I have to cast my vote for Machine Learning For Dummies just because of its precise clarity and slow teaching style.
But to get us started let’s dive into a somewhat tangential topic: AI programming.
If you’re looking for something up-to-date on AI development I’d recommend Paradigms of Artificial Intelligence Programming by Peter Norvig.
This is widely regarded as one of the best programming books ever written. The actual writing style is very easy to follow. It guides you on a path of self-discovery. But the examples also help you learn in the clearest manner possible with techniques for writing quality Lisp.
Please note that you should have programming experience before opening this book. You do not need to be an expert, but if you have never written code(or aren’t very good yet) then you’ll probably struggle through this text.
However it is long at 900+ pages and it’s indisputably the best resource for learning about AI, a very related subject comparable to machine learning.
I earnestly hate recommending the “for dummies” series of books because I find them too light and rudimentary. However this one is an exception because the authors are both data scientists with years of experience.
Machine Learning For Dummies is truly the guide for anyone that simply cannot grasp machine learning. The book uses Python code examples but you don’t really need to know Python to make your way through this text.
You’ll learn a deep history of machine learning and the differences between machine learning and AI. The authors take their time and write very carefully to explain every single point with great detail.
You can pick up this book with just a background in math & logic, no hardcore programming experience required. If you’ve never looked into algos before you might struggle but can still get by if you do some complementary research.
This is perhaps the newest book in this whole article and it’s listed for good reason. Machine Learning: The New AI looks into the algorithms used on data sets and helps programmers write codes to learn from these datasets.
The author Ethem Alpaydin is a well-known scholar in the field who also published Introduction to Machine Learning. Ethem has the experience to teach this subject and his prior writing is top notch.
Ethem explains how machine learning has evolved over the years and how it’s currently used in practical applications. You should have a background in programming to complete this book, however you can get started with just a mere interest in machine learning.
And since it’s one of the newest books in this list you can be sure it’s up-to-date and very relevant to the current tech industry.
While machine learning and AI are quite different, they share many concepts that can help programmers on their journey. You should understand how to approach these problems in order to solve & scale machine learning projects.
This is why Artificial Intelligence: A Modern Approach can be a wonderful introduction. The book totals 1100+ pages and covers a wide variety of techniques.
Advanced developers or even intermediate-level programmers may wince at the book’s simplicity. This is not meant for anyone with prior experience. It’s truly made for the novice just getting started, and this can be perfect to whet your appetite for AI and the basics of machine learning.
Machine Learning by Peter Flach covers practical examples of machine learning in action. I’d say this book is made for intermediate-to-advanced developers who want a “back to the basics” approach to machine learning that goes into a greater amount of detail than other books.
You learn about statistical models that can be generated, analyzed, and predicted with machine learning techniques. Peter includes an overview of a custom spam filter explaining how this works and why it has advanced so much in recent years.
Topics get much deeper with ROC analysis playing a big role in later chapters.
The book is full of graphs, charts, and diagrams to help explain each point. Machine learning is a vast subject and Peter does a great job of breaking down the main components through example.
If you’re big into data science or machine learning then I’d highly recommend this book, but only if you have some background in the topic.
Very few books I’ve mentioned so far have been language specific. Well Sebastian Raschka’s 450+ page tome Python Machine Learning is the first to break this cycle.
It is the best introductory guide for Python developers who want to get into machine learning.
A good portion of programmers start with Python because it has simple syntax, it’s very powerful, and because it has a wide range of machine learning libraries like scikit-learn.
This book goes into great detail about Scikit-learn and how to apply it to data analysis. The author recommends visualization along with algorithm development so you’ll learn not only how to compute data, but how to visualize it too.
Definitely a technical book but not made just for Python experts. If you have some background in Python and maybe a teensy bit of experience with scikit-learn then you’ll radically improve your knowledge by picking up this book.
Yet another Python-based learning book, although this one is a bit shorter and much more detailed with examples. Data Science from Scratch covers an intro to Python before even getting to the code. So even if you’re fairly inept you should be able to work through this.
However I would specifically recommend this book for intermediate-to-advanced developers who already know what they’re doing in Python. You certainly don’t need to know any machine learning or data/statistics analysis(hence the “from scratch” in the title).
Writing style is clear and precise. The level of depth is not as great as Python Machine Learning, although truthfully both books will have you delving deep into machine learning so neither one is a bad choice.
My favorite aspect of this book is the coding style. Each snippet builds on previous work and the author carefully explains what he’s doing each step of the way.
Although you can’t tell just by looking at the title, this book also teaches you how to build in Python. Tariq Rashid explains neural networks as a fundamental component of machine learning and his book is the best way to dive into it.
Make Your Own Neural Network is brilliant and affordable. But you do need some experience with Python to feel comfortable going through everything.
However the author’s goal is to slowly build up your understanding of neural networks through live examples. You do not need to be an expert to get into this book. However you do need ambition and drive to push through the tough parts.
Thankfully the author’s writing style is gentle and easy to follow, so you shouldn’t have too many hangups. Neural Networks are tough to crack but with this book you shouldn’t need anything else.
This title is a mouthful and it’s also one of the pricier options you can get. But the level of specificity is great and coming from MIT Press I’m excited to say this book is a powerhouse.
Fundamentals of Machine Learning for Predictive Data Analytics explains the process of analyzing & selecting datasets based on relationships and custom algorithms. This covers more generic informational learning that takes in related info from other resources in a dataset. But this book also covers more complex probability-based machine learning too.
You’ll study very advanced concepts of developing data analysis through machine learning that actually works in the way you want it to. It teaches through example and little mini tutorials that force you to consider different methods of teaching through data.
You will definitely need a solid background in programming and mathematics to pick up this book. I’d only recommend this to data scientists and programmers who already understand machine learning but want to take that next step further.
Here’s another book that works best for advanced data scientists/developers. Each chapter covers an ever-advancing topic on probability and machine learning based on patterns in datasets.
Pattern Recognition and Machine Learning is the definitive guide to mastering pattern recognition. It takes you from a general intro through live examples using very basic ideas to get the point across.
There is no talking down to any reader with this writing style. The authors tend to repeat themselves just to drive a point home. So while this is a tough subject, this book is also the best resource to really drill these concepts into your brain.
You will need a heavy background in math and even knowledge of data science to work your way through this. It won’t be easy. But with the simple writing style and the clear examples in this book you should walk away with a much deeper understanding of pattern recognition for practical machine learning.
These ten books are my top picks and they cover a wide range of subjects. You’ll need to decide which one fits best for your current situation, but it may be worth grabbing a couple if they can help you advance a project or help you better understand a comp sci class.
For raw machine learning from scratch I’d recommend Machine Learning For Dummies. If you’re willing to dive into Python then consider Python Machine Learning instead.
Both of these can work for beginners but I think starting with a programming language can help you understand the concepts in a real-world scenario.
All the other advanced books are very technical and difficult to recommend with broad strokes. But skim through this list again and if anything catches your eye be sure to check it out.
Alex is a fullstack developer with years of experience working in digital agencies and as a freelancer. He writes about educational resources and tools for programmers building the future of the web.
