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Best Machine Learning Books

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Best Machine Learning books
Best Machine Learning books for beginners to advanced

Are you looking for the best machine learning books that would help you in establishing your career as a machine learning developer? 

Do you wish to know how vast machine learning is and how its concepts and applications are applied in various technological fields? Then you have landed on the correct article. 

Machine learning is a subset of artificial intelligence that has the ability to work on its own with minimal or no supervision from humans. 

We see machine learning in our daily lives so much that we often forget how life was before its development. We see machine learning in healthcare industries, educational institutions like schools and universities, commercial organizations, IT industries, e-commerce sites, finance and trading, and many more. 

With the rise of machine learning in almost every field, it has become one of the highest-paying jobs in the world. 

Several IT companies and MNCs like Google, Yahoo, Facebook, Twitter, Pinterest, and many more hire the best machine learning developers, analysts, data scientists, programmers, etc on a regular basis. 

That being said, it is essential that you get started with the fundamentals of machine learning, deep learning, Python, data science along with other programming languages and terms so that you can proceed with the advanced and more complex topics. 

This article has a combined list of the best machine learning books that will help you to kickstart your career in various machine learning fields. 

Whether you are looking for the best machine learning books for beginners, intermediate or advanced learners, this article has got your back! So let’s get started!

Book 1: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow is a book written by Aurélien Géron, a machine learning trainer and consultant who established companies like Wifirst, Polyconseil, and Kiwisoft. 

The author has written this book for programmers that want to learn, understand and integrate vast machine learning concepts in several applications of their company like financial data, production data, HR reports, hotline stats, user logs, and many more. 

As this book is listed as one of the best Machine learning books, it teaches from basic techniques like linear regression to advanced Deep learning topics that are widely used in machine learning using Python Libraries like Keras, TensorFlow, and Scikit-Learn. 

This book lets you explore the best of machine learning training models like support vector machines, decision trees, random forests, and ensemble methods. 

Additionally, you will also learn to use the TensorFlow library to create and train neural net architectures like convolutional nets, recurrent nets, and deep reinforcement learning. 

After successfully completing this book, you will be able to create the best marketing strategies to get potential customers by customization of their previous buying patterns and records as well as detecting fraudulent activities and many more. 

Rated as one of the top machine learning books for beginners, it is more beneficial if you have some knowledge of NumPy, pandas, and matplotlib as well as mathematical concepts like statistics, calculus, algebra, and probability. 


  • The author starts with traditional machine learning techniques with several practical approaches and contexts as it transits to deep learning concepts. 
  • It has several problems and practices related to the real world to give you hands-on experience. 
  • It works both as a guide and reference.
  • This book covers most of the fresh advancements in machine learning and related technological fields. 
  • It is well suited for any level of learners.  


  • The mathematical fonts may give an unpleasant experience for those who are reading it in Kindle edition. 
  • According to a few readers, there are some missing codes that are not fixed.

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An Introduction to Statistical Learning:

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications.

Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more.

Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.

Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience.

This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.

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The Elements of Statistical Learning: Data Mining, Inference, and Prediction

This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics.

Many examples are given, with a liberal use of colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning.

The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book.

This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorisation, and spectral clustering.

There is also a chapter on methods for “wide” data (p bigger than n), including multiple testing and false discovery rates.

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AI and Machine Learning for Coders: A Programmer’s Guide to Artificial Intelligence

AI and Machine Learning for Coders is a book written for coders who want to establish a career as an AI specialist as it focuses on how neural networks are applied to solve concepts involving computer vision, natural language processing, sequence modeling, and so on. 

It is written by Laurence Moroney, a leading AI advocate in Google who is heavily passionate about artificial intelligence using machine learning. 

Mr. Moroney has covered several important machine learning implementations on applications like computer vision, natural language processing as well as web, mobile, cloud, and embedded runtimes for sequence modeling. 

The book starts off teaching ways to apply TensorFlow in developing vast architectural models. Having knowledge on Python is beneficial for this as in this way, you will understand how NumPy works for numeric calculations. 

Later on it proceeds with how TensorFlow models can be used in models like developing apps in Kotlin with the help of Android studio and development apps in Swift with Xcode with the help of iOS. 

To conclude, you will learn to develop models using TensorFlow, learn to work with code samples, implementation of computer vision and feature detection in images as well as apply NLP to sequence and tokenize sentences and words, and much more. 

Anyone who is interested to learn about machine learning and artificial intelligence, irrespective of their learning level, can get their hands-on this best Machine Learning book. 


  • It explains the working and usefulness of TensorFlow at various applications and problems. 
  • The working of neural networks at a high level is also explained well. 
  • Coders will greatly benefit from this book. 
  • The author also exclusively discusses Google’s artificial intelligence principles as well as fairness and interpretability at the end. 
  • Sufficient examples and exercises that are based on real world activities are used to help you understand the concepts better.

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Pattern Recognition and Machine Learning (Information Science and Statistics)

Pattern Recognition and Machine Learning is a book written by Chris Bishop, a senior distinguished scientist and laboratory director at Microsoft Research Cambridge. 

Mr. Bishop has written this book to explain the Bayesian analysis that assists in pattern recognition. 

This way, the book conveys approximate inference algorithms that give quick approximate solutions to problems where the outcomes are not directly feasible. 

It has many graphical models which assist in conveying the concepts of probability distributions. Additionally, it has more than 400 examples and exercises that aim to provide solid information and analysis on machine learning, statistics, computer vision, data mining, and many more. 

This is considered as one of the best machine learning books as along with delivering the best of knowledge related to Bayesian methods in learning more about pattern recognition, it also covers all the recent advancements in technologies that will let the learner be updated with the ongoing projects. 

This book is ideally intended for advanced undergraduates, graduates, researchers, practitioners as well as 1st year Ph.D. students, irrespective of their knowledge on pattern recognition or machine learning concepts. However, basic knowledge of multivariate calculus and linear algebra is needed along with some experience working with applications of probability. 


  • This book is reviewed to be well suited for understanding for advanced undergraduates and 1st year PhD students as well as others. 
  • This book is not mathematically heavy so there’s a balanced approach. 
  • This book has tons of exercises and examples for illustration of problems. 


  • The proof that several types of loss lead to condition median or mode is not properly elaborated, rather it is left as an exercise. 
  • There’s not much information given clustering apart from k-means. 
  • Without basic knowledge on linear algebra, probability, calculus, probability, and so on, you won’t get much help from this book. 
  • Many readers said the topics are not well explained as much of the topics are left to understand through the means of exercises and practices. 

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Practical Machine Learning for Computer Vision: End-to-End Machine Learning for Images

Practical Machine Learning for Computer Vision is a book that emphasizes the application of machine learning in healthcare, manufacturing, details, and much more through the means of computer visions and images. 

It is written by Valliappa Lakshmanan, Margin Görner, and Ryan Gillard, all working at reputed organizations like Google and TensorFlow, who wish to deliver knowledge on how machine learning models help in the identification of objects in images and visuals. This way, the readers will know how to solve problems that have the classification, detection, measurement, segmentation, representation, counting, and many more using machine learning. 

As you read on, you will learn ways to design machine learning models for computer vision tasks as well as do testing for training models that are written in TensorFlow and Keras. 

With the help of this, you will understand how dataset creation, data reprocessing, model design, model training, evaluation, deployment, interpretation as well as basics of deep learning and natural language processing works. 

In the end, you will be able to use models like ResNet, SqueezeNet, or EfficientNet to do your task, developing end to end machine learning pipelines for explaining training, evaluating, and deploying your model as well as preprocess data augmentation and support learnability for images and integrating the best AI advancements in your models and many more. 

Although this is not one of the machine learning books for beginners, it is mainly written for software engineers who use TensorFlow and Keras to apply machine learning to object detection on images and solve usual computer vision use cases. 


  • This machine learning book starts with the fundamentals and proceeds with the advanced topics in a smooth and easy to understand approach. 
  • It helps you to understand well and clearly about how machine learning is essential in object detection and computer vision techniques. 
  • It has several examples and practices that help you develop the machine learning models with accurate execution. 


  • Some readers commented that the images are black and white, unlike other books published by O’Reilly. 
  • This book is on the expensive side, however it’s worth the money, according to many readers. 
  • All the notebooks are available in GitHub and it can be run in Google Colab as well as Google Cloud’s Vertex notebooks. 

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Machine Learning Guide for Oil and Gas Using Python: A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications

Machine Learning Guide for Oil and Gas Using Python is a book based on ways to apply machine learning in the oil and gas industry with the help of Python. 

It is written by Hoss Belyadi, founder and CEO of Obsertelligence LLC with more than a decade of experience in unconventional and conventional reservoirs, and Dr. Alireza Haghighat, who is currently a senior technical advisor at IHS Markit in the Engineering Solutions Dept. 

Based on their decades-long experience working in oil and gas reservoirs, the author has written this book to provide support to engineers who want to apply the knowledge of machine learning using Python in this industry of oil and gas. 

Listed as one of the best machine books, it starts off by demonstrating the ways Python works and proceeding further with its applications in well testing, shale reservoirs, and production optimization, all essential components in the oil and gas industry. 

This book is an ideal catch for those petroleum engineers who are looking to understand how Python works at a fundamental level and integrate its applications and algorithms in various situations and problems that arise in the petroleum industry with a balanced approach to both theoretical and mathematical terms. 


  • This book is ideal in understanding the way machine learning and Python works in the oil and gas industry. 
  • There are several visuals and graphs that give in-depth analysis. 
  • The Jupyter notebooks can be downloaded by the readers. 
  • Illustration of several examples and practices. 
  • The applications of machine learning are taught in a step by step process. 


  • A few readers said that there’s a lack of examples related to DCA treatment and Bayesian analysis. 

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Hands-On Quantum Machine Learning With Python: Volume 1: Get Started

Hands-On Quantum Machine Learning With Python is a book written by Dr. Frank Zickert who wants people to know about the various possibilities and applications of machine learning algorithms on quantum computing. 

Regarded as one of the best machine learning books for quantum machine learning algorithms, you will know how to deal with problems related to the current advancements in computing technologies that are essential in developing solutions for real-world practical problems. 

As this book has a well-balanced approach in explaining and illustrating theoretical aspects and practical approaches, it is ideal for anyone who is interested to learn about quantum computing. Hence, it has earned itself as a reputed title of being one of the best machine learning books for beginners. 


  • This book is well suited for beginners who are getting started with quantum computing of machine learning algorithms as well as those who are already an established senior machine learning developer. 
  • The development and applications of algorithms are explained in depth. 
  • There are several real world exercises and practices used to help you in understanding the concepts better. 
  • The Quality codes are well explained. 

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Graph Machine Learning: Take graph data to the next level by applying machine learning techniques and algorithms

Graph Machine Learning is a book written by Claudio Stamile, Aldo Marzullo, and Enrico Deusebio, all of whom are established machine learning developers working at prominent companies. 

The authors have written this book to explain how machine learning techniques and algorithms are implemented in graphs, understand relations between various modes to improve business strategies as well as apply machine learning in a graph-based approach to tackle real-world issues. 

As you will read about the various possibilities and applications of machine learning in graph data that involves many supervised and unsupervised applications, you will also learn a lot about developing machine learning pipelines like data processing, model training, prediction, extraction of data from social media networks, text analytics, natural language processing and financial transactions using graphs to uncover the full of possibilities and potentials using graph data. 

Some of the major topics covered in this book are writing Python scripts for extracting features from graphs, knowing the distinguishing factors of main graph representations techniques, knowledge of shallow embedding methods, graph neural networks, graph regularization methods, and so on. 

You will finally be able to deploy and measure the application that you develop in an efficient way. 

This is one of the best machine learning books for beginners as only beginner-level knowledge of graph data and databases is needed. Apart from that, this book is mainly intended for data scientists, graph professionals, graph analysts, data analysts as well as machine learning developers who are working on machine learning-driven graph databases. 


  • Many readers commented and appreciated that the explanations and scripting examples are of high quality that are easy to understand. 
  • This book is well suited for researchers and data scientists. 
  • It has several introductory codes that give a clear fundamental understanding. 
  • The book is full of examples and real world practices. 


  • The graph theory discussions and concepts could have been more detailed. 
  • If you don’t have basic knowledge on machine learning, it will be difficult for you to understand. 
  • As the images are black and white, some readers will not be comfortable in reading some topics. 

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Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition

Python Machine Learning is a book that derives how fundamental techniques can be applied in research or industrial applications as it focuses on practical code examples. It is written by Sebastian Raschka and Vahid Mirjalili, both pioneers in the world of Python and machine learning. 

This book takes you through the practical approaches of Python and machine learning with the help of updated information TensorFlow 2, Generative Adversarial Network models, reinforcement learning, and many more. 

It acts both as a step-by-step guide and reference as it has several explanations, visualizations, and working examples of various machine learning methods. That being said, as it covers TensorFlow 2.0, it has all the updated information on the API features of Keras and Scikit-learn which helps in deep learning. 

Once you finish reading this one of the best machine learning books on our list, you will be able to implement machine learning techniques on image classification, sentiment analysis, intelligent web applications, as well as developing and training neural networks, GANs, etc. 

You will also be able to know the best ways to evaluate and train the models as well as use regression analysis for predicting continuous target results.  

This book is ideal for Python developers, machine learning developers, and data scientists who aspire to develop practical machine learning and deep learning code by extending their knowledge on applications of machine learning. Hence, this book is not really a machine learning book for beginners with no prior experience. 


  • It is ideal for data scientists, machine learning and Python developers. 
  • Deep learning and TensorFlow applications are well elaborated. 
  • Regression analysis is well explained with many examples. 
  • Perfect balance of mathematical and practical examples. 


  • The Kindle edition is much preferred for this book as the print version is not proper according to some readers.

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Art in the Age of Machine Learning (Leonardo)

Art in the Age of Machine Learning is a book based on the application of machine learning in artistic practices. It is written by Sofian Audry who is an artist, scholar, and professor at the University of Quebec Canada. 

This book focuses on ways machine learning is beneficial for media artists, musicians, composers, writers, and many more in the field of media installation, robotic art, visual art, electronic music and sound, and so on. 

It shows how machine learning is applied in practices like cybernetics art, artificial life art, evolutionary art, etc. Some of the essential topics covered by this book are ways artists capture the preparation process by playing with evaluative capacities; talks about teachable machines and models, clarifying how various sorts of machine learning frameworks empower various types of creative practices; and audits the job of information in AI craftsmanship. 

It also shows how specialists use information as a natural substance to control learning frameworks and contending that AI takes into account novel types of algorithmic modified variations. 

As illustrated from the title of this book, this book is well suited and ideal for scholars and artists of various artistic fields. 


  • It is one of the best machine learning books specifically written for scholars, artists and students of art who want to involve the best of machine learning algorithms in the field of art and media. 
  • It clarifies several myths like machine learning can do art without artists and that it will soon be on the levels of superhuman creativity and innovation. 
  • It works well as theoretical and methodological guidance.

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Introduction to Machine Learning with Python: A Guide for Data Scientists

Introduction to Machine Learning with Python is a book based on developing machine learning solutions on your own. It is written by Andreas Müller and Sarah, both data scientists working in machine learning. 

Regarded as one of the best Python books, it is more algorithm-focused rather than the mathematical aspects because of its approach to the Scikit-learn library. 

You will learn about the basic concepts and applications of machine learning, benefits, and drawbacks of machine learning algorithms, a procession of data using machine learning, evolution and parameter tuning of models along with the concept of pipelines for chaining models and encapsulating workflow. 

Additionally, you will learn about text-specific processing methods and overall polish your machine learning and data science skills. 

Knowledge of NumPy and matplotlib libraries are a bonus in understanding this book better. 


  • The code implementation is based mainly on Python modules rather than custom programming which is ideal for those into Python. 
  • This book is very comprehensive in its approach. 
  • It is well organized with separate chapters for each topic. 
  • It doesn’t overburden the readers with unnecessary complex models. 


  • As it doesn’t focus heavily on mathematical concepts, some explanations may feel like they are lacking. 
  • Complex topics, wherever written, are not well illustrated. 
  • It is in black and white version so it may be annoying for some readers. 

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Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python, 2nd Edition

Machine Learning for Algorithmic Trading is a book written by Stefan Jansen for those who want to apply machine learning in finance and trading. 

As a unique approach towards covering machine learning fundamentals and trading, it serves as a guide to using ML such as using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio in finance and market fields. 

Learning with the practical examples and practicing the exercises in this machine learning book, you will be able to develop, evaluate and train automated trading strategies along with predictive modeling for trading decisions. 

As this book is mainly written for data analysts, data scientists, Python developers, investment analysts, and portfolio managers, if you are one of these with some knowledge on Python and machine learning, then you will be able to develop machine learning predictions into trading strategies by the end of this book. 


  • It helps greatly in building trading strategies, asset portfolios, and more. 
  • It helps you to develop great predictive models for trading. 
  • New and intermediate learners as well as practitioners will get help from this book. 


  • The instructions on the examples are not in proper order so installation becomes difficult. 
  • Many unnecessary talks are given which are not really relevant in today’s world. 
  • Few technical terms are wrongly derived. 

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Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)

Machine Learning: A Probabilistic Perspective is a book written by Kevin P. Murphy, a Senior Staff Research Scientist at Google Research who wishes to introduce people to how machine learning uses both probabilistic inference and models as a unifying technique. 

As it is known to all, machine learning helps in the detection of patterns in the data given to further use it to identify future data with the help of uncovered patterns as well. But to understand how all these processes work, it is essential to know about probability, optimization, and linear algebra as well as discussion of recent developments in conditional random fields, L1 regularization, and deep learning. This book helps you with that. 

It lets you uncover all the mentioned topics with the help of colored visuals and graphs along with practical examples and practices from application domains such as biology, text processing, computer vision, and robotics.

Additionally, most of the models are MATLAB software packages implemented, ie, a probabilistic modeling toolkit (PMTK) which are available online, free to use. 

This book is mainly written for advanced undergraduates and 1st-year graduate students who have basic college-level mathematical knowledge. 


  1. Unlike what it suggests, this book is good for expert machine learning analysts and developers. 
  2. This book serves as a comprehensive framework. 
  3. There are several technical approaches used in this book which helps in giving practical experience. 


  • Many reviews mentioned that the content of this book is not organized well and the topics covered are difficult to understand. 
  • This book might be confusing and difficult to understand for students. 
  • Many topics are merely discussed but are not illustrated with clear definitions. 

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Mathematics for Machine Learning

Mathematics for Machine Learning is a book written by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong, all established data scientists and AI analysts, aimed at providing knowledge on concepts of machine learning involving mathematical practices like linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability, and statistics.

The authors have taken great measures to balance the gap between mathematical and machine learning texts with minimum prerequisites. 

With the help of this book, you will get your concepts cleared on linear regression, principal component analysis, Gaussian mixture models, and support vector machines. 

All of the concepts come with illustrations of examples and exercises that are practical in approaches so that the learners can use and polish their skills to the best of their abilities. 

This book is written for students, researchers, developers, and many who want to gain knowledge on applying mathematical concepts through the means of machine learning, irrespective of their knowledge of math. 


  • This is one of the best machine learning books for beginners because of its minimum prerequisites to get started with this book. 
  • The topics covered in this book are well illustrated with clear cut precision that doesn’t confuse the readers, especially beginners. 
  • It is a good reference for calculations that many find hard to solve. 


  • The quizzes lack the answers so readers may get confused on whether their answers are right or wrong. 
  • You need to have your basics on mathematics clear for better understanding. Otherwise, this book won’t be of much help. 

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Here comes an end to the best machine learning books list. 

Although there are several more books that may be relevant and more beneficial for you based on your learning capacity, we hope that you’ve enjoyed reading and learning from our article. 

Let us know if you know of any other interesting books about machine learning by connecting with us through the comment box and our social media handles.