AI and Machine learning (ML) technologies are rapidly evolving. I mean, we all have witnessed the disruption that the AI and ML revolution are bringing to our world in every sector. Also, according to Google, “Machine Learning is the future.”
It’s this growth and projections that have seen ML undoubtedly become the most in-demand technology of this era! We can agree there’s such a high demand for ML skills; and that it’s an exciting time to learn ML.
Whether you want to implement ML algorithms as a developer, become a data scientist, or apply cutting-edge ML skills to your business analysis and intelligence, you can pick up applied machine learning skills through a couple of books — much faster than you might think! I also wrote a 5-step series on how to become a machine learning engineer from my experience.
In this article, I review the best and highly sought Machine Learning books you can read in 2021.
This is one of the books I used when I started learning Machine Learning. Years later, I still reference back to it. If you don’t have lots of knowledge and experience with Python, the book is an excellent place to get started.
The authors Andreas C. Müller and Sarah Guido have written it in a well-organized and easy-to-follow structure. It also comes with hands-on examples that help you learn the steps necessary to understand and create successful machine-learning applications with Python and the scikit-learn library. You’ll love the fact that it is packed with Python-based code implementations.
Even as a beginner, the book will teach you practical ways to build your own ML models
Ideal for beginners, Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig is filled with everything you might want to know as far as AI is concerned. The book explains concepts very well, and it is clear to see why it is the foundation for numerous university-level AI courses.
It is easy to understand and loaded with examples that help you pick the meaning of AI jargon such as overfitting, loss functions, adversarial networks, etc.
It also features remarkable practical implications in the field of AI, such as machine translation, speech recognition, robotics, and more. If you want to start delving into AI and machine learning, this is an excellent book to introduce you to the concepts!
What more, the authors have an open-source repository on Github with implementations and tutorials on algorithms from the book.
Another great Machine Learning read, this book is absurdly good! The author, Aurélien Géron assumes that you know close to nothing about Machine Learning and takes you through concepts, intuitions, and the necessary tools you need to implement programs capable of learning from data.
Like its name, the book provides a hands-on approach to learning by doing and covers many beginner and advanced techniques.
The book is also loaded with tips and tricks for hacking machine learning. Whether you are looking to build predictive models in Python, or are a software developer looking to become an ML engineer, Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow comes highly recommended and won’t disappoint!
To master machine learning concepts, the Hundred-Page Machine Learning Book by Andriy Burkov is also highly recommended. The book is pretty straightforward and crisp in explaining basic mathematical concepts, principles, and essential machine learning fundamentals. Just a few chapters in the book, and you’ll realize you are holding a machine learning gem in your hands.
As a practical guidebook, the most comfortable thing is that it will get you started, and you can execute Machine Learning within a few days (even as a beginner).
It is organized such that the first beginning chapters get you started with the basics with the subsequent chapters introducing you to advanced topics. It’s suitable for engineers who want to learn ML in little time without learning a professional degree program.
With neural nets being an essential element of deep learning and artificial intelligence and yet not so easy to understand, you’ll be happy to get and own a copy of this book.
Here’s why — it’s so clear, concise, and is a perfect blend of simplicity and ML information. The author, Tariq Rashid, is a master communicator who presents the book so that the reader has no choice but to reach the point of understanding.
Step-by-step, he takes you through a neural network’s mathematics, starting from elementary ideas and gradually building up an understanding of how neural networks work.
It’s a well-written book with lots of explanatory images, charts, graphs, and complete source code of a working neural network without any required prior knowledge of complicated math or any deep learning theory.
For an excellent and straightforward start in Machine Learning, a book that will get you up and running is Machine Learning for Absolute Beginners by Oliver Theobald.
It is highlighted by Tableau as the first of “7 Books About Machine Learning for Beginners.”
The book is a relatively quick read balanced with text and figures, providing a high-level overview of machine learning concepts, models, algorithms, and more. The visual examples and clear instructions made the rest of the book a breeze to follow.
One of the best features of this book is many real-world scenarios that make it suitable for any beginner to enjoy and understand theoretical and practical concepts that help get started on a career in Machine Learning.
Mathematics for Machine Learning is an excellent read that comes highly recommended. It has been termed as one of the ‘Best books you can come across for starting your Machine Learning journey.’
Once it’s in your hands, it will start you off with the basics using clear examples and explanations. The book then quickly moves you into the intermediate level with relevant information and concrete practicals, helping you gain a deep understanding of machine learning. To finish, Mathematics for Machine Learning provides valuable guidance towards advanced ML topics.
The good news: Every chapter in the book includes worked examples and exercises to test your understanding. Also, programming tutorials are offered on the book’s website — how cool is that?
While the author, Marc Peter Deisenroth, assumes very little knowledge from the readers, you must have crucial mathematical concepts such as linear algebra, probability, statistics, and calculus. But worry not, you can always grasp those by making a few detours on information to help you understand the math more even as a beginner. The author has given references to where to do further studies. I would say, this is one of the best books if you are looking to study the math behind machine learning!
If you want to understand the key principles in Data Science, this book has got you covered with its excellent and fantastic introduction. Specifically, anyone with basic Python programming, algebra, statistics, and probability will find this book a great introduction to Data Science.
The author Joel Grus, does an excellent job explaining the topics using his signature brand of humor, keeping the read entertaining even in the most advanced areas. Instead of teaching you how to use high-level libraries like Pandas and Scikit-Learn, Data Science from Scratch teaches you essential data science concepts by having you write simple functions yourself so you can understand the exact underlying process.
And what other better way to learn than to do it yourself. The result is that you’ll walk away with a much more robust understanding of fundamental concepts necessary to understand data science and machine learning. Here’s the kindle version.
Have you wondered how AI will impact our society in the future, especially after COVID-19? Are you worried that AI will take your job? This book by Lasse Rouhiainen presents well-researched and documented discoveries about how technological breakthroughs in AI will change our world.
The book stands out as exceptional as an essential roadmap for guiding the next generation. You’ll like it if you want straightforward explanations of complex issues, broad-ranging applications, and real-world examples.
Applied Artificial Intelligence for Business Leaders by Mariya Yao, Adelyn Zhou, and Marlene Jia is the book to get for anyone who wants to get a hand on how AI is shaping the business landscape today. Specifically, business owners who are interested in AI will find Applied Artificial Intelligence an excellent resource!
It is a quick read and useful guide with densely packed information for business leaders looking to understand more about artificial intelligence for business, enterprise applications, and recommended implementation strategies.
It helps the reader clarify a lot of AI terminology and clarify common errors with data and machine learning. It also comes highly recommended as an informative book suitable for both technical and non-technical business executives!
Thank you for reading! I value your comments and shares. Let me know of any AI/ML that has made a huge impact on your learning journey and I’ll add it to the list.
Some links in this article are affiliate links. This means if you decide to make a purchase, I’ll get a small commission at no extra cost to you. Note that I included high-quality books that I have gone through as well as those that are highly recommended in the field.
For updates on the most recent and interesting Machine Learning research papers and trends out there, subscribe to AI Scholar Weekly. Cheers!