21 Best Machine Learning Books for Beginners and Experts

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21 Best Machine Learning Books for Beginners and Experts
List of the best machine learning books every aspirant must read on their machine learning journey to advance their careers in machine learning.
Last Updated: 13 Oct 2022
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Learning machine learning is easy and quick, and you can learn through machine learning courses, videos, bootcamps, tutorials, and of course, good machine learning books! Though there are claims all over the Internet that you can become a data scientist or a machine learning engineer in 30 days, ProjectPro experts suggest that you take time to sink in the foundational concepts of machine learning step by step, work on diverse machine learning projects to apply what you’ve read in a book or learned in a video. To help you choose a well-structured learning path for machine learning, we have narrowed it down to the 21 best machine learning books and 50+ Machine Learning Projects for anyone who wants to make it big in the industry as a data science or machine learning practitioner. Each project and book is recommended by ProjectPro’s industry experts, making them the richest sources of practical knowledge in the world of machine learning. So, let’s get started! 
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FAQs on Machine Learning Books 
21 Best Machine Learning Books to Learn Machine Learning
Listed below are the best machine learning books for beginners to experts with focus areas such as Python , R , Deep Learning , and Artificial Intelligence . These books will help you jumpstart your machine learning career and help you along the way. So, let us start with the best machine-learning books for beginners before moving on to complex books. 
3 Best Machine Learning Books for Beginners
Here are three of the best machine-learning books for beginners: 
Machine Learning For Absolute Beginners: A Plain English Introduction - Oliver Theobald 
It's a good book for beginners who want to learn machine learning. You don't even need a background in coding, mathematics, and statistics to start reading this book.  
Exclusive Topics Covered 
Building Machine Learning models 
Why read this book?
The book provides precise explanations and visual examples accompanying each machine-learning algorithm . This makes the concepts more approachable for beginners to understand the fundamentals of machine learning. 
Most Popular Review of the Book
"An excellent introduction to machine learning, in which the author describes what machine learning is, techniques and algorithms, and the future of & resources for machine learning learners." - An Experienced Machine Learning Professional 
The Hundred-Page Machine Learning Book - Andriy Burkov 
This book presents a solid introduction to machine learning in just a hundred pages. The book not just provides an understanding of machine learning concepts but also delves into the different types of it, such as supervised learning, unsupervised learning, and reinforcement learning. 
Exclusive Topics covered: 
Basic and Advanced Practice 
Why read this book? 
Several top professionals recommend this book, including Peter Norvig (Director of Research at Google), Aurélien Geron (Senior AI Engineer, author of a bestseller Hands-On Machine Learning with Scikit-Learn and TensorFlow), Karolis Urbonas (Head of Machine Learning at Amazon), and Sujeet Varakhedi (Head of Engineering at eBay). 
Most Popular Review of the Book 
"Burkov has undertaken a beneficial but impossibly hard task in reducing all machine learning to 100 pages. He succeeds well in choosing the topics - both theory and practice - that will be useful to practitioners, and for the reader who understands that this is the first 100 (or 150) pages you will read, not the last, and provides a solid introduction to the field." - by Peter Norvig , Research Director at Google. 
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Why read this book? 
Along with providing machine learning knowledge, the book also blends theoretical foundations with practical realities of developing tools for data analysis . Also, the author of this book - Peter Harrington, is a professional data scientist and holds five US patents, and his work has appeared in several academic journals.
Review of the Book
"Must buy for anyone wanting to dive deep into Machine Learning. An excellent book that introduces ML algorithms and many common ML algorithms are introduced and implemented. The book may appear a bit complex for someone who just started machine learning" - an Industry Leader.
3 Best Advanced Machine Learning Books
Find below the most popular advanced machine-learning books: 
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started - Drew Conway 
This book is for an experienced machine learning engineer interested in Data-crunching. If you have a good grasp of R, you should consider reading this book because it concentrates on data analysis in R and even touches on using advanced R for data wrangling. 
Exclusive Topics covered:
Write simple machine learning algorithms in R
Why read this book? 
The book features case studies to show practical applications of machine learning algorithms, which help in integrating mathematical theory into reality—for example, building a "whom to follow" recommendation system from Twitter data. 
Most Popular Review of the Book
"Machine Learning for Hackers" is a comprehensive book on using R to do machine learning. One is left with an evident notion that R is one of the best possible tools for all researchers in data science" - by an Experienced Programmer. 
Deep Learning (Adaptive Computation and ML Series) - by Ian Goodfellow, Yoshua Bengio, and Aaron Courville 
Several experts consider this to be the best book on deep learning. It covers all the fundamental deep learning concepts and offers a friendly introduction for those interested in deep learning. 
Exclusive Topics Covered 
Mathematical concepts including linear algebra, probability and information theory, and numerical computation. 
Deep learning techniques, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, and computer vision.
Natural language processing 
Why read this book?
This book is written by three innovative and prolific researchers in the field - Ian Goodfellow (Research Scientist at Google), Yoshua Bengio (Professor of Computer Science at the Université de Montréal), and Aaron Courville (Assistant Professor of Computer Science at the Université de Montréal). Not just this, the book is also recommended by highly experienced industry experts such as Yann LeCun , Director of AI Research, Facebook, and Elon Musk, cochair of OpenAI; co-founder and CEO of Tesla and SpaceX. 
Review of the Book
"Written by three experts in the field, Deep Learning is the only comprehensive book on the subject."—Elon Musk, cochair of OpenAI; co-founder and CEO of Tesla and SpaceX. 
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Pattern Recognition and Machine Learning (Information Science and Statistics) - Christopher M. Bishop 
This is the first pattern recognition textbook to introduce the Bayesian viewpoint. The book is suitable for computer science, data mining, machine learning, bioinformatics, signal processing, and statistics courses. But you should know the fundamental concepts of multivariate calculus, basic linear algebra, and data science before reading this book. 
Exclusive Topics Covered
Combining models 
Why read this book?
It is the only book that uses graphical models to describe probability distributions, while no other book does so. The book also offers approximate inference methods that provide quick approximations in cases when exact solutions are impractical. 
Most Popular Review of the Book 
"This beautifully produced book is intended for advanced undergraduates, Ph.D. students, researchers, and practitioners, primarily in machine learning or allied areas. A strong feature is the use of geometric illustration and intuition. This impressive and interesting book might form the basis of several advanced statistics courses. It would be a good choice for a reading group." - John Maindonald for the Journal of Statistical Software. 
3 Best Machine Learning Books with Python 
The following are the best three machine learning books with python: 
Introduction to Machine Learning with Python: A Guide for Data Scientists - Andreas C. Müller & Sarah Guido
This is one of the perfect books for data scientists who have proficiency in Python and want to learn machine learning. It enables you to create powerful machine-learning applications using open-source Python libraries such as Numpy, Scikit-learn, Pandas, and Matplotlib. Thus, a basic understanding of these libraries can help you get more from this book. 
Exclusive Topics Covered 
Advantages and disadvantages of common machine learning algorithms
Data processing, including which data aspects to focus on
Workflow methods for working with text data
Recommendations to improve machine learning and data science skills 
Why read this book?
Along with the fundamentals of machine learning, the book also covers advanced methods for hyperparameter tuning and model evaluation. Additionally, the book also features the complete workflow of a machine learning project for improved business problem demands. 
Most Popular Review of the Book
"This is a great book. For anyone with some basic understanding of linear algebra/statistics, the authors can present all the important details without using equations and, more importantly, how they all relate to one another." - an Experienced Professional 
Python for Data Analysis - Wes McKinney
This book is best for analysts and python programmers new to data science and scientific computing. It covers complete guidelines for manipulating, processing, cleaning , and crunching datasets in Python. 
Exclusive topics covered: 
Basic and Advanced NumPy features 
Why read this book? 
This book comes with helpful case studies that show how to address various data analysis problems quickly. It also covers the problems in web analytics, finance, and economics through detailed examples. 
Most Popular Review of the book: 
"A comprehensive book with a lot of details in data wrangling, it has been taught step by step, so there is no confusion in figuring out the codes; the author explained the complex python subjects very intuitively, so anyone can read this book, and learn data wrangling"- said by a Professional on Goodreads. 
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Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow - Sebastian Raschka and Vahid Mirjalili 
This book extensively studies machine learning and deep learning in Python. It contains all fundamental machine learning techniques with clear explanations, graphics, and working examples. In addition, it helps you build machine-learning systems in the real world. 
Exclusive Topics Covered 
Build neural networks, GANs, and other models 
Sentimental analysis , image classification, and intelligent web applications
Predict target outcomes using regression analysis 
Evaluating and tuning models 
Why read this book? 
This book also delves deep into sentiment analysis, a subset of natural language processing (NLP) that teaches you how to categorize documents using machine learning algorithms.
Most Popular Review of the Book 
"Python Machine Learning is a highly practical, hands-on book that covers the field of machine learning, from theory to practice. I strongly recommend it to any practitioner who wishes to become an expert in machine learning. Excellent book!" - Sebastian Thrun , CEO of Kitty Hawk Corporation and chairman and co-founder of Udacity. 
3 Best R Machine Learning Books
Find below the best R machine learning textbooks: 
R in Action - Robert Kabacoff
R in Action is a language tutorial book that focuses on real-world applications. It shows how to use the R programming language through examples relevant to scientific, technical, and business developers. 
Exclusive Topics Covered 
Interfacing R with other software
Data visualization with R
Why read this book? 
This book is a blend of both R systems and the use cases. It includes elegant approaches for dealing with messy and incomplete data using typical statistical analysis methods. It also helps you master R's comprehensive graphical features for presenting data visually.   
Most Popular Review of the Book 
"The book is great in many ways. It combines the different aspects of R very well and switches entertainingly between applications of descriptive statistics and visualizations. Having experience in software development and programming, I found the book easy to read and understand." - Data Analyst. 
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Deep Learning with R by Francois Chollet, J. J. Allaire
This book briefly introduces deep learning using the powerful Keras library and its R language interface. It also helps you practice your skills with R-based applications in computer vision, generative models, and natural-language processing. 
Exclusive Topics Covered 
Deep learning for sequences and text 
Why read this book?
Deep Learning with R not just provides an introduction to the field of deep learning but also helps you understand deep learning through logical justifications and real-world examples. 
Most Popular Review of the Book 
"Excellent - one of the best introductions to deep learning available, especially if your background is R rather than Python. The underlying theory is explained clearly, with lots of worked examples." - An experienced Professional. 
An Introduction to Statistical Learning: With Applications in R - Gareth James, Trevor Hastie, Robert Tibshirani, and Daniela Witten
This book covers an overview of the field of statistical learning and helps with the process of understanding and managing complex datasets. 
Exclusive Topics Covered 
Shrinkage approaches 
Why read this book? 
You should read this book as each chapter implements the methods in R, a popular open-source statistical software platform.
Most Popular Review of the Book 
"An Introduction to Statistical Learning (ISL)" by James, Witten, Hastie, and Tibshirani is the "how to" manual for statistical learning. Inspired by "The Elements of Statistical Learning" (Hastie, Tibshirani, and Friedman), this book provides clear and intuitive guidance on implementing cutting-edge statistical and machine learning methods. ISL makes modern methods accessible to a broad audience without requiring a background in Statistics or Computer Science. The authors give precise, practical explanations of the available techniques and when to use them, including explicit R code. Anyone who wants to analyze complex data should own this book intelligently." — Larry Wasserman, Professor, Department of Statistics and Machine Learning Department, Carnegie Mellon University. 
3 Best Machine Learning Books for Free 
Explore the best three machine learning textbooks for free below: 
Neural Networks and Deep Learning - Michael Nielsen
It is a free online book that teaches the core concepts behind neural networks and deep learning. The book also provides solutions to problems based on speech recognition, image processing, and natural language processing. 
Exclusive Topics Covered 
Deep learning 
Why read this book? 
This book will help you with a solid foundation for using neural networks and deep learning to solve complex pattern recognition problems. 
Most Popular Review of the Book 
"All-in-all, I highly recommend Neural Network and Deep Learning to any beginning and intermediate learners of deep learning. Suppose this is the first time you have learned backpropagation. In that case, NNDL is a great general introductory book." - Arthur Chan, Principal Speech Architect at Medallia, Administrator of Facebook AIDL Group, Ex-Maintainer of CMU Sphinx.  
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Machine Learning Yearning - Andrew NG
This book focuses on everything from the data science process to data visualization. You will discover how to set up development and test sets in this book and how to agree on ML strategies in a team setting. 
Exclusive Topics Covered 
Error Analysis
Why read this book? 
This book not just covers machine learning strategies but also provides you with a deep understanding of modern machine learning projects. 
Most Popular Review of the Book
"Hallmark of Andrew's teachings is the ability to present the most complex concepts in the simplest possible terms without losing the essence of the lesson. This book is an exemplar of that ability. This book is like a timeless cookbook for those designing ML systems from the ground up. I highly recommend this book to those seeking clarity in designing ML solutions." - said a Professional on Goodreads. 
Understanding Machine Learning: From Theory to Algorithms - Shai Shalev-Shwartz, and Shai Ben-David
As the name suggests, this book covers the fundamental concepts of machine learning followed by various learning algorithms from theory to algorithms. It also contains a wide variety of learning models and advanced concepts. 
Exclusive Topics Covered 
Compression Bounds 
Why read this book? 
This book is highly recommended by industry experts such as Bernhard Schölkopf (Scientist at Max Planck Institute, Germany), Avrim Blum (Carnegie Mellon University), and Peter L. Bartlett (University of California, Berkeley). 
Most Popular Review of the book: 
"This elegant book covers rigorous theory and practical machine learning methods. This makes it a unique resource, ideal for all who want to understand how to find structure in data." - Bernhard Schölkopf , Scientist at Max Planck Institute, Germany.
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3 Best Books on Machine Learning and AI 
Check out the best books on artificial intelligence and machine learning below: 
Artificial Intelligence: A Modern Approach - Stuart J. Russell and Peter Norvig
This book presents the basic conceptual theory of artificial intelligence. It also covers the essential applications of AI technology, such as machine translation, autonomous vehicles, speech recognition, and household robotics. 
Exclusive topics covered: 
Natural Language Processing, Perception, and Robotics 
Why read this book?
This book serves as a comprehensive reference guide for beginners and benefits students taking artificial intelligence courses at the undergraduate or graduate levels. 
Most Popular Review of the book: 
"Of all the AI books I have read, this one is arguably the most accessible to undergrads (CS, EE background). It assumes only minimal mathematical formalities, and pretty much the maths things are self-contained. The authors did a great job of keeping the contents up-to-date with the latest happenings in AI while keeping the readers sane. Overall, thumbs up!" - said a machine learning engineer on Goodreads. 
Applied Artificial Intelligence: A Handbook For Business Leaders - Mariya Yao, Adelyn Zhou, and Marlene Jia
This practical guide is especially for business leaders passionate about leveraging machine intelligence to improve organizational productivity. The book also helps you make business decisions using the applications of Artificial Intelligence and machine learning.
Exclusive Topics Covered 
Business Intelligence and analytics 
Why read this book?
This book has been selected as a CES 2018 top technology book of the year. Not only that, but the book also teaches business leaders how to successfully lead AI initiatives by selecting the right opportunities, performing strategic experiments, building a diverse team of experts, and actively designing solutions to benefit both your organization and society.
Most Popular Review of the book: 
"Finding resources on how to think about AI applied to the industry is difficult. So far, this is the only book I've found on the subject. It takes a high-level approach to offer a whirlwind tour of the many facets of AI and tries to detail how they may be applied to business." - by a professional on Goodreads. 
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Linear regression 
Why read this book?
This book includes a programming sample for each chapter and explains all algorithms using actual numeric calculations. 
Most Popular Review of the book: 
"Good introduction to AI (machine learning) problems modeling and optimization" - an experienced Professional 
3 Best Practical Machine Learning Books
The following are the best practical machine learning books. 
Programming Collective Intelligence: Building Smart Web 2.0 Applications - Toby Segaran 
This book is more like a practical guide than an introduction to machine learning. It teaches you how to create machine learning algorithms to collect data useful for specific projects. 
Exclusive Topics Covered
Methods to detect groups or patterns
Non-negative matrix factorization
Why read this book?
Every chapter of this book contains exercises for extending the algorithms, where the code for each algorithm can be found on blog posts, websites, or Wikipedia.
Most Popular Review of the book: 
"Bravo! I cannot think of a better way for a developer to learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details." -  Dan Russel , Research Scientist at Google. 
Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems - Aurélien Géron
This book is the best resource for developing project-based technical skills in machine learning. It combines theory and practices with a broad range of project examples using python libraries such as TensorFlow, Scikit-Learn, and Keras. 
Exclusive topics covered:
Algorithm fundamentals 
Why read this book?
The book is written such that each chapter features exercises to help you apply what you have learned, making it an excellent way to get started with machine learning.
Most Popular Review of the book: 
"This is the best book I've read on machine learning. It is well written, and the examples are very good with real data sets. The first half is an introduction to machine learning, and the second half explores deep learning. It is a great hands-on machine learning book to read along an online course." -  by a Professional on Goodreads.
Fundamentals of Machine Learning for Predictive Data Analytics - John D. Kelleher, Brian Mac Namee, and Aoife D'Arcy
This book combines machine learning theory with practical applications and case studies. It presents machine learning applications using predictive data analysis and accompanies each learning concept with a working example. 
Exclusive Topics covered: 
Similarity-based learning
Why read this book? 
This book covers all machine learning principles, delving into the subject's theory and illustrating it with practical applications, working examples, and case studies.
Most Popular Review of the book: 
"One of the best ML books out there. Dives deep into the practical implementation of Sklearn and Tensorflow. Also, dives deep enough into the math side of ML. Read it from cover to cover. Really worth it." - a Professional on Goodreads. 
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Key Takeaways
There is no lack of learning resources on the web for machine learning engineers and data scientists who want to upgrade their skills on their machine learning journey. While working through the books on machine learning in this blog, remember that learning machine learning would be without practical application and hands-on experience. So, if you need help enhancing your machine learning skills, do not forget to explore  250+ solved end-to-end data science and machine learning projects on the ProjectPro repository. These projects will take you one step ahead on your machine-learning journey.
FAQs on Machine Learning Books 
What are the best machine learning books for beginners?
The best machine learning books for beginners are: 
Machine Learning For Absolute Beginners: A Plain English Introduction - Oliver Theobald 
The Hundred-Page Machine Learning Book - Andriy Burkov 
Machine Learning in Action - Peter Harrington 
What are the top 10 books on machine learning out there?
The top 10 books on machine learning are: 
Machine Learning For Absolute Beginners: A Plain English Introduction - Oliver Theobald
The Hundred-Page Machine Learning Book - Andriy Burkov
Python for Data Analysis - Wes McKinney
Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems - Aurélien Géron
Programming Collective Intelligence: Building Smart Web 2.0 Applications - Toby Segaran 
Understanding machine learning: From theory to Algorithms - Shai Shalev-Shwartz, and Shai Ben-David
An Introduction to Statistical Learning: With Applications in R - Gareth James, Trevor Hastie, Robert Tibshirani, and Daniela Witten
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started - Drew Conway 
Pattern Recognition and Machine Learning (Information Science and Statistics) - Christopher M. Bishop 
Introduction to Machine Learning with Python: A Guide for Data Scientists - Andreas C. Müller & Sarah Guido.