Machine learning is on everyone’s lips nowadays. This article lists the best machine learning books out there as of 2019.
Anyone is able to get into machine learning due to the huge amounts books and learning material available today. These books are some of our personal favorites and a good place to get started.
All books have a difficulty rating to reflect the complexity of the material as well as the prerequisites of the user.
1: The book is for anyone. 5: The book is for skilled AI engineers and data scientists.
Life 3.0 – Being Human in the Age of Artificial Intelligence
In this extremely well-written book, Max Tegmark is answer some of the questions everyone have about artificial intelligence. You know how every other sci-fi movie these days are about a superintelligence taking over the world? To Max Tegmark this is not necessarily sci-fi! Don’t get me wrong – it’s not some “AI dangerous, AI bad” propaganda, but it is a very thoughtful discussion on whether or not we will be able to control a general artificial intelligence. What happens if (when) we invent something that is smarter than ourselves? And what makes us think that we can control it?
Life 3.0 – Being Human in the Age of Artificial Intelligence is not only giving you an idea of what an AI powered future can look like. It prepares you for the transformation and teaches you how to best fit into this world of artificial intelligence.
You can get the book here.
Machine Learning Yearning
Author Andrew Ng
You can’t learn about machine learning without running into this guy at least once. Andrew Ng is an entrepreneur, former Vice President and Chief Scientist of Baidu, adjunct professor at Stanford University, data scientist and one of the leading experts in AI and machine learning. In this book, you will learn to understand how AI and machine learning algorithms actually work rather than learning a bunch of algorithms by heart. The book will teach you which algorithms to apply to which problems – and how to do it!
The book is highly focus on strategic structuring of machine learning projects and is written for engineers, programmers and data scientists. One of the core concepts of the book is to iterate continuously towards a solution. You should always build a working prototype as soon as possible so you can determine how to best spend your time. As soon as you have a working prototype model, you can start to investigate the mistakes that it makes, look into your training and test accuracies, plot the learning curve, and more. All this can help you focus on the things that improves your model the most. A lot of the ideas in this book build upon this principle.
Highlighted topics of Machine Learning Yearning
- Best way to set up an AI project
- Making the best choices when optimizing an ML algorithm
- Analyse, understand and minimize errors
- Deal with complex situations
- Compare systems to human-level performance (and above)
- Understand when to use end-to-end learning, transfer learning and multi-task learning
Not only is this one of the best introductory machine learning books of its time, it is also available for free right here.
Some of the questions answered in Life 3.0 – Being Human in the Age of Artificial Intelligence
- After automating more and more jobs in the future, how do we ensure the peoples sense of purpose prevails?
- Will autonomous and intelligent weapon systems inevitably lead to disasters?
- How do we guard AI against hacking, crashing or malfunctioning?
- Will AI be the savior of man kind and help us flourish like never before?
- … Or will it eventually replace us altogether?
Life 3.0 is easy to pick up for anyone interested in AI. However, it still manages to discuss some of the most important and exciting topics of this era of artificial intelligence. This book is a must read for anyone interested in AI and the future of our race.
Artificial Intelligence: A New Synthesis
Ready for something a bit more challenging? Then Nils J Nilsson’s masterpiece from 1998 won’t disappoint you. Despite being more than 20 years old, the books stays highly relevant to this day. It deals with all major areas of artificial intelligence while introducing most important subfields such as machine learning.
Nilsson discusses the entire pool of artificial intelligence: From simple software that can apply rules and solve highly repetitive tasks, to more complicated ones that deduce knowledge, prove hypothesis, learn patterns, and – eventually – self-manages.
Click here to get the book.
Highlighted topics of Artificial Intelligence: A New Synthesis
- Neural networks
- Propositional and first order predicate calculus
- Computer vision and more deep neural networks
- Heuristic search
- Genetic programming
- Knowledge representation and reasoning
- Bayes networks
The book is very well written, but it does require a solid knowledge of computer science for the best reading experience.
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
This book stands a bit out from this list by being something a professor would suggest for his college class. Hand-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems is – apart from being a really long title for a book – the ideal place to start if already know the basics of machine learning. The book will teach you how to design and implement your own machine learning algorithms and systems with Python. You can find more about machine learning in python here.
You don’t get to be an Amazon Bestseller without being the real deal. This book will not only be a great read, it will also work perfectly as a look-up tool for future machine learning projects.
Pattern Recognition and Machine Learning
Here is another somewhat more advanced machine learning book. This one requires a certain knowledge of college mathematics to read beneficially. Linear algebra, probability, calculus, and preferably some statistics. That being said, no understanding of pattern recognition or Machine Learning itself is required, which makes it a good introduction for people with some mathematical / statistical background. It will provide a very in-depth understanding of key topics, specifically in Machine Learning and Bayesian methods.
Maybe you already read some of these popular machine learning books, but hopefully you will be able to find at least some that catch your interest here. Or maybe you thought we forgot something? Don’t forget to drop a comment here or on Facebook either way.