Best Machine Learning Books
The 7 Top Books to Advance Your Skills, From Beginner To Expert
Image © Dave Chenell
Whether you're new to machine learning or a seasoned data scientist, the machine learning books below can help you become an authority in the world of big data and data mining. Let’s look at the seven best machine learning books you should consider adding to your library.
Best Machine Learning Book (Overall Staff Pick):
Summary:
In this excellent book, Aurélien Géron shows readers how to build intelligent machine learning systems using Python machine learning frameworks like Scikit and TensorFlow. Each chapter includes quick exercises that test your knowledge of the concepts that have been discussed. With a combination of detailed examples, various deep learning techniques and tools, this book is an excellent introduction to deep learning.
Topics Covered:
Techniques for training and scaling deep neural nets
Tracking machine-learning projects end-to-end with Scikit.
Building and training neural nets with TensorFlow
Exploring training models like support vector machines, decision trees, and random forests.
Neural net architectures
Skill Level:
Intermediate — Some programming experience is beneficial, but not required, to get started with this book.
What People Like About the Book:
As the name suggests, this book provides practical knowledge of different deep learning techniques, plus links to other relevant learning materials. It's a good way to stay up to date on developments in machine learning.
What Could Be Improved:
If you're brand new to machine learning, you may find it difficult to follow a few examples in this book. That being said, overall we found it easy to understand, and remains our top book on the topic.
Book Information:
Author: Aurélien Géron
Edition: Second
Year Published: 2019
Formats: Paperback, Kindle
To read an excerpt or browse reviews:
Best Machine Learning Books For Beginners:
1. Machine Learning For Absolute Beginners: A Plain English Intro
Summary:
If you're just getting into the world of machine learning, this book is a great place to start. Written for first-timers, Machine Learning For Absolute Beginners (second edition) breaks down complex algorithms using engaging visual examples plus clear explanations that are easy to grasp.
Topics Covered:
Data scrubbing Techniques
How to download free datasets
Clustering and Regression Analysis
How to build your first Machine Learning Model using Python
Improving your Machine Learning Model with Bias/Variance
Essential tools and machine learning libraries
Skill Level:
Beginner — Even if you have never written a line of code, you will easily follow through with the concepts in this book.
What People Love About the Book
Readers love how this book slows down to explain complex machine learning concepts using practical, relatable examples and tutorials.
What People Do Not Like About the Book
For a book written for absolute beginners, Machine Learning for Absolute Beginners throws in too much machine learning vocabulary. Some readers describe it as unclear and difficult to understand.
Book Information:
Author: Oliver Theobald
Edition: Third
Year Published: 2021
Formats: Paperback, Kindle
To read an excerpt or browse reviews:
2. Machine Learning Refined: Foundations, Algorithms, and Applications
Summary:
Jeremy Watt leverages his experience as a machine learning professor to create this exhaustive guide for machine learning enthusiasts. In this book, you'll learn how to conduct machine learning research and build data-driven products. Machine Learning Refined contains more than 300 simplified illustrations plus 100+ programming exercises to help you understand machine learning algorithms.
Topics Covered
Natural language processing
Recommender Systems
Algorithmic Thinking
Machine Learning methods
Linear and non-linear methods
Skill Level:
Beginner — Great for anyone looking for fundamental knowledge in machine learning.
Why Readers Like It:
It combines machine learning theories and practical implementation very well. It also covers a lot of materials.
Book Information:
Author: Jeremy Watt
Edition: Second
Year Published: 2020
Formats: Hardcover, eTextbook
Read an excerpt or browse review:
Best Machine Learning Books For Intermediate Coders:
1. The Hundred-Page Machine Learning Book
Summary:
Want to understand machine learning in only 100 pages? Andrly Burkov has actually made it possible in The Hundred-Page Machine Learning Book. He explores different machine learning topics and answers practical questions you'd deal with at the beginning of any machine learning project. You'll also find lots of tools and links to resources for further reading.
Topics Covered
Fundamentals of Machine Learning
Neural networks and deep learning
Supervised and unsupervised learning
Reinforcement learning
Skill Level:
Intermediate — The book requires some programming experience and knowledge of mathematical algorithms.
What Readers Like About the Book:
It breaks down many machine learning concepts in a simple, easy-to-understand way.
What Can Be Improved:
As with any book on the topic, machine learning newbies may need to re-read a few of the mathematical algorithms in the book before moving on.
Book Information:
Author: Andriy Burkov
Edition: First
Year Published: 2019
Formats: Hardcover, Paperback, Kindle
To read an excerpt or browse reviews:
2. Deep Learning (Adaptive Computation and Machine Learning series)
Summary:
This book offers a comprehensive knowledge of machine learning and adaptive computation in data analysis from deep learning techniques to research perspectives. You'd learn about different machine learning applications, deep generative models, and algorithms. It also has a website filled with additional learning resources and case studies for readers.
Topics Covered:
Linear Factor Models
Industry Techniques including sequence modeling, convolutional networks, and deep feedforward networks
Structured probabilistic models
Skill Level:
Beginner – Intermediate level. This book is a great choice for software developers, computer science undergraduates, and graduate students who love research.
What People Like About the Book:
For the most part, the book is clear with concise information about fundamental deep learning concepts.
What Could Be Improved:
The book focuses on linear algebra and other mathematical algorithms that deep learning newbies may find complex. Some explanations could benefit from more concrete examples and illustrations.
Book Information:
Author: Ian Goodfellow
Edition: Illustrated edition
Year Published: 2016
Formats: Hardcover, Kindle
To read an excerpt or browse reviews:
3. Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a Phd
Summary:
Dive into deep learning with this practical guide for programmers. In this book, Howard and Gugger show coders how to achieve impressive results quickly using fastai and Pytorch. This is one of the fastest paths to creating real AI applications with little code, small amounts of data, and basic math knowledge.
Topics Covered:
How deep learning models work
Turning models into web applications in the real-world
The latest deep learning techniques, including linear regression and optimization
Implementing deep learning algorithms
Skill Level:
Intermediate — To get the most out of this book, you should have some basic knowledge of a programming language like Python.
What Readers Like About This Book:
Deep Learning for Coders with Fastai and PyTorch has lots of practical examples. It will immediately put your knowledge to work. It also uses fastai, which was specifically developed to help accelerate and simplify the process of launching real AI applications.
What Could Be Improved:
Even though the authors say this book is for everyone, you should have at least a basic knowledge of math. A bit of Python experience will also help you cruise through it more quickly.
Book Information:
Author: Jeremy Howard
Edition: First
Year Published: 2020
Formats: Paperback, Kindle
To read an excerpt or browse reviews:
Best Machine Learning Book For Experts
AI and Machine Learning for Coders: A Programmer's Guide to Artificial Intelligence
Summary:
In this book, you'll learn about different AI and Machine Learning concepts like computer vision and national language processing. Using a code-first approach, AI and Machine Learning for Coders is designed for programmers who want to transition to AI. You'd also learn how to build data architectures with different frameworks.
Topics Covered:
How to embed models in Android and IOS
How to build models with TensorFlow
How to implement computer vision in images
How to use natural language processing (NLP)
Machine learning using coding samples
Skill Level:
Advanced — This is for programmers who already have foundational machine learning knowledge.
What Readers Like About the Book:
It explains complex machine learning techniques in a straightforward and relatable way.
What Could Be Improved:
If you’re into deeply technical information, this book doesn’t spend as much time on the precise mathematics and algorithms underpinning AI.
Book Information:
Author: Laurence Moroney
Edition: First
Year Published: 2020
Formats: Paperback, Kindle
To read an excerpt or browse reviews:
Summary
Go ahead and choose any of our machine learning book recommendations to boost your knowledge. You can buy all these books on Amazon and start learning today. Many are also available for Kindle, or on Audiobook.