Machine learning is in the new frontier of science and IT. Though in its infancy still, this field has garnered a huge amount of attention from professionals and the general public. Business owners seem to be getting in on the train, as well. If you’d like to introduce automation to your company or just to explore machine learning in more depth, reading quality machine learning books can help you a great deal. All you need is an open mind and willingness to learn.
Quick Look - The Best Machine Learning Books
- Machine Learning for Beginners by Alec Gilliam - Get this book
- Machine Learning by Rudolph Russell - Get this book
- Applied Artificial Intelligence by Mariya Yao, Adelyn Zhou, Marlene Jia - Get this book
- Practical Artificial Intelligence by Arnaldo Castano - Get this book
- Machine Learning by Matthew Harper - Get this book
- Pragmatic AI by Noah Gift - Get this book
- Neural Networks and Deep Learning for Beginners by Pat Nakamoto - Get this book
- Machine Learning for Mobile by Revathi Gopalakrishnan, Avinash Venkateswarlu - Get this book
- Machine Learning on Google Cloud Platform by Alexis Perrier, Giuseppe Ciaburro, V Kishore Ayyadevara - Get this book
- Building Machine Learning Systems with Python by Luis Pedro Coelho, Willi Richert - Get this book
What You Should Look for in a Machine Learning Book
There are several key considerations to make before deciding on a book. First, you should consider the author’s reputation. Next, you may want to decide on the area of machine learning that you’d like to learn more about. Finally, you should consider the price of the book.
When shopping for quality machine learning books, you should first check the author’s reputation. If you’re after a book on how to build machine learning systems, make sure you pick a book by an author with extensive experience in big machine learning projects.
On the other hand, if you’re looking for books on how to introduce machine learning and automation to your company, search for authors who’ve successfully done that.
There are three main types of machine learning – supervised, unsupervised, and reinforcement learning.
Many books explore and utilize only one type of machine learning, so you’ll need to decide in advance which you’d like to learn more about. If you want to learn about all three, buying multiple books might be the best approach.
A quality book on machine learning doesn’t have to be expensive. There are many affordable and well-written books on the subject.
However, an investment in a pricier book might sometimes pay off. That’s mainly because pricier books tend to be more detailed and thorough.
Our Picks for the Best Machine Learning Books
Here are our top ten books on machine learning. The list was created based on the criteria laid out in the previous section.
1. Machine Learning for Beginners by Alec Gilliam
- Who’s it for? Beginners
- Price: On Sale
In the words of the author, Machine Learning might open the doors for the future where machines might be able to adapt and respond to a huge array of stimuli without direct programming.
Gilliam covers the basic concepts of machine learning in a light and easy-to-digest manner. He offers concise and clear explanations and gives great examples of the practical achievements of machine learning.
In Machine Learning for Beginners, you’ll find an expanded definition of machine learning, how it works, common approaches, common terms, association rule learning, computational learning, deep learning, pattern recognition, neural networks, supervised algorithms, unsupervised algorithms, and more.
2. Machine Learning by Rudolph Russell
- Who’s it for? Intermediate
- Price: On sale
Rudolph Russell wrote this excellent guide for all who are interested in how to create machine learning systems. His platform of choice is Python, one of the most accessible and potent programming languages. Scikit-learn library is his weapon of choice.
Russell guides the reader through the entire process of making a machine learning system step by step. He masterfully combines in-depth theory and practical advice in real-life situations and problems that may arise during the process.
This book contains numerous illustrations and easy-to-follow explanations of key concepts and terms. It also contains exercises for you to apply what you’ve learned.
3. Applied Artificial Intelligence by Mariya Yao, Adelyn Zhou, Marlene Jia
Machine learning and artificial intelligence are here to stay. Some see them as the enemy that will leave a slew of joblessness in its wake, while others regard them as the solution to humanity’s every problem.
Instead of discussing the future and possible outcomes of the AI hype, Yao, Zhou, and Jia focus on today’s practical applications of AI and machine learning, and how they can be used to benefit business leaders, their companies, and the world.
This book won’t teach you the technical and theoretical intricacies of building a machine learning system. Instead, it will show you how to recognize and seize good opportunities in machine learning-enhanced AI.
4.Practical Artificial Intelligence by Arnaldo Castano
A lot of mystery surrounds the field of machine learning and artificial intelligence. Many people think that it is just another sci-fi gimmick that doesn’t have any impact or practical application in their lives.
Between the covers of Practical Artificial Intelligence, Arnaldo Castano reveals to the readers how deeply AI has been inserted into our everyday lives and how common it is. He also briefly covers all the important concepts and types of machine learning without getting too technical.
Practical Artificial Intelligence is for people who wish to understand how AI and machine learning work in real life and how they can be implemented wisely.
5. Machine Learning by Matthew Harper
Matthew Harper’s Machine Learning is a set of two books on the inner workings of building machine learning systems. It is among the most recommended works in the field, as well as one of the most in-depth.
The first book explores neural networks and how they influence our daily lives and their application in machine learning. It also breaks down the basics of machine learning in an easy-to-digest way.
Book two is concerned with deep learning. It will teach you the basic principles of deep learning, how it works, and how big tech companies like Twitter, Facebook, and Amazon use it.
6. Pragmatic AI by Noah Gift
With the unstoppable advance of artificial intelligence and machine learning, managers and CEOs should embrace it and learn how to best apply it in their companies. That’s where Noah Gift’s Pragmatic AI steps in.
On the pages of this book, Gift offers a practical, no-nonsense guide to AI for business leaders and decision-makers. He explains the ins and outs of working with off-the-shelf systems and building your own with Python.
Pragmatic AI will guide you through the entire process of planning, leading, and completing the project of building AI and machine learning systems for your company.
7. Neural Networks and Deep Learning for Beginners by Pat Nakamoto
Pat Nakamoto’s Neural Networks and Deep Learning is one of the best-selling books in the machine learning field. It is the first of three books in a series.
At the start, Nakamoto gives the reader a super-simple visual explanation of deep learning and its basic terms and concepts. After the introduction, he tackles topics such as how to build a deep neural network from the ground up, McCulloch-Pitts neuron, types of algorithms used in machine learning systems, prominent types of DNN, and many more.
8. Machine Learning for Mobile by Revathi Gopalakrishnan, Avinash Venkateswarlu
- Who’s it for? Intermediate
- Price: On sale
As its title suggests, Machine Learning for Mobile deals with how to build smart apps for mobile operating systems. Due to their market domination, only Android and iOS systems are covered.
Gopalakrishnan and Venkateswarlu teach the readers how to build apps for iOS and Android devices and how to use TensorFlow Lite, Core ML and other popular machine learning toolkits. Mobile-friendly cloud services are also covered.
The authors offer a complete walkthrough of the app-building process for both iOS and Android platforms, including the common pitfalls and problems.
9. Machine Learning on Google Cloud Platform by Alexis Perrier, Giuseppe Ciaburro, V Kishore Ayyadevara
Machine Learning on Google Cloud Platform is among the leading books on machine learning for the cloud. You can expect to learn how to build data-based applications for mobile, web, and dashboards for Google Cloud.
You’ll also learn how to make, train, and optimize models for different tasks, use premade TensorFlow models, build your own Keras and TensorFlow models, and much more. Prerequisites include knowledge of command line, bash shell, as well as Python scripts. It is also desirable to have a basic understanding of data science and machine learning.
10.Building Machine Learning Systems with Python by Luis Pedro Coelho, Willi Richert
This book stands out among the myriad books on the topic due to its clear explanations and no-frills approach to machine learning. Coelho and Richert use the latest and most popular libraries and datasets available, keeping the reader completely up to date with current trends.
With this book, you will learn how to make a classification system for different applications, use Amazon Web Services, use Scikit and TensorFlow to solve regression-based problems, build product recommendation systems, and much more.
Finding the Best Machine Learning Books
You should pick books on machine learning based on what you need. If you’re a CEO who wants to modernize your company, you should read books about management and machine learning.
On the other hand, if you’re interested in building a machine learning system, technical books are for you.