21 Best Machine Learning Books of All Time

Machine learning has given humanity the power to implement tasks automatically. Machine learning refers to studying computer algorithms and statistical models for a task utilizing patterns and inference rather than explicit instructions.

Do you know that Machine Learning is one of the hottest career options? Indeed research shows that Machine Learning Engineer is the best job in 2021, with a growth pace of 344% and an average base salary of $146,085 a year.

Obviously, machine learning is a challenging field, but that doesn’t mean you cannot learn it. To support you, we have compiled a collection of 21 hands-on machine learning books in the following article.

1. Introduction to Machine Learning with Python

If you are a data scientist excellent at using Python and concerned with learning machine language, the Introduction to Machine Learning with Python: A Guide for Data Scientists will be a perfect book for you.

This book will show you multiple hands-on ways of creating your own machine learning methods. You’ll learn about all the crucial steps for building robust machine learning applications with the use of Python and Scikit-learn library. Besides, gaining a deep understanding of matplotlib and NumPy libraries will help fasten the learning process effectively.

The reference source includes:

• Advanced approaches for model evaluation and parameter tuning
• Pipelines for encapsulating workflow and chaining models
• Applications, basic concepts of machine learning
• Practices for working with text data
• Representation of processed data
• Machine learning algorithms

2. Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow

The second edition of this machine learning book includes Keras to its content lists, along with TensorFlow and Scikit-learn. It offers an intuitive comprehension of the different concepts and tools that you need to grow intelligent systems.

You’ll need programming experience to understand the Hands-on Machine Learning book. Each chap demonstrates plenty of exercises that help you apply what you have learned.

Topics discussed in the book:

• Deep neural networks
• Training models, including decision trees, random forests, support vector machines, and ensemble methods
• Training neural nets
• Linear regression
• Deep reinforcement learning

3. Deep Reinforcement Learning Hands-on

Maxim Lapan is a deep learning enthusiast who is interested in hands-on applications of Deep Reinforcement Learning. The book covers the newest DL tools and their limitations. The readers have a chance to grasp the evaluation approaches, including policy gradients and Cross-entropy, with examples on Atari set of family favorites and virtual games like Connect4.

The book can help readers learn:

• Evaluate RL methods including Cross-entropy, TRPO, PPO, Actor-Critic, D4PG, DQN, DDPG, and more
• Generate your OpenAI environment to train a stock trading agent
• Instruct your agent to play Connect4 using AlphaGo Zero
• Learn the newest in-depth RL research on topics including AI-driven chatbots
• Defeat Atari arcade games using the value iteration method

4. Machine Learning for Absolute Beginners

You have had no experience and exposure to machine learning before but desired to learn it? Then you should pick the Machine Learning for Absolute Beginners book. You can get benefits from this reference without having any coding or mathematical background.

Through this book, readers can understand the most toned-down definition of machine learning and related concepts. Plus, clear explanations and visual examples, as well as different ML algorithms, are added to make sure readers follow the book quickly.

The reference source covers:

• Clustering
• Feature engineering
• Regression analysis
• Cross-validation
• Fundamentals of neural networks
• Ensemble modeling
• Data scrubbing techniques

5. Machine Learning: The New AI

Machine Learning offers an incredible variety of applications in modern times, from product recommendations to voice recognition and self-driving cars. The basis of machine learning is data, and data has grown bigger, so machine learning is considered fundamental in the process of turning data into knowledge.

Machine Learning – The New AI book concentrates on basic machine learning, ranging from the evolution to essential learning algorithms and their example applications.

It also covers:

• Machine learning algorithms for pattern recognition
• The ethical and legal implications of machine learning for data security and privacy
• Artificial neural networks
• Reinforcement learning

6. Fundamentals of Machine Learning for Predictive Data Analytics

If you have grasped machine learning fundamentals and now want to discover Predictive Data Analytics, you should not miss this book. Machine learning can be utilized to create predictive models by taking patterns from large datasets. With the use of theoretical concepts and practical applications, the book provides a detailed analysis of this application of machine learning. Moreover, the author also discusses the Predictive Data Analytics trajectory, from data to insight to decision.

In addition, Fundamentals of Machine Learning for Predictive Data Analytics book includes four different approaches machine learning:

• Information-based learning
• Probability-based learning
• Error-based learning
• Similarity-based learning

Each of them is followed by a non-technical conceptual explanation, mathematical models, and algorithms.

7. Machine Learning for Dummies

It seems to be impossible to manage stuff like web search results, automation, fraud detection, real-time ads on web pages, and spam filtering without machine learning. This book comes in handy, helping you kickstart your machine learning journey.

Machine Learning for Dummies will enable you to “speak” some languages, such as R and Python. Then you can set up machines to manage pattern-oriented tasks and data analysis. Furthermore, you also can know how to code in Python using Anaconda and in R using R Studio.

Some topics included in the book are:

• Introducing how machines learn
• Preparing your learning tools
• Getting started with the math basics
• Learning from smart and big data
• Applying learning to real problems

8. Pattern Recognition and Machine Learning

This book is great for you to dig deep into the world of Pattern Recognition and Machine Learning. It is the first documentation that introduced the Bayesian viewpoint on pattern recognition. Besides, the book analyzes difficult topics that require some knowledge of basic linear algebra, multivariate, and data science.

The reference book has increasing difficulty level chapters on machine learning and probability according to patterns in datasets. It begins with the general introduction of Pattern Recognition followed by straightforward examples.

9. Machine Learning for Hackers

Let’s clarify a little bit that the Hacker in the title means an excellent programmer, not a secretive computer cracker.

This paperback is suitable for any programmer engrossed in data crunching. It helps you start with machine learning by using multiple practical case studies instead of dull math-heavy presentations.

Machine Learning for Hackers demonstrates certain issues in each chapter, such as classification, optimization, prediction, and recommendation. It trains you to analyze multiple sample datasets and create simple machine learning algorithms in the R programming language.

10. Programming Collective Intelligence

If you want to grasp and exploit the power behind search rankings, social bookmarking, product recommendations, or online matchmaking, this book is ideal for you. It shows how you can create multiple applications for Web 2.0 to harness the huge amount of data that is generated by about 3 billion users on the Internet.

Programming Collective Intelligence does this using machine learning and helps you explore user experience, personal taste, marketing, and human behavior. Additionally, all of the machine learning algorithms are presented with the code that can be used anywhere, including your website, blog, Wiki, or even a specific application.

The book includes some topics:

• Introduction to Collective Intelligence
• Searching and ranking
• Optimization
• Modeling with decision trees
• Building price models
• Finding independent features

11. Machine Learning

This machine learning book is highly recommended for those who are at the intermediate or expert level and want a “back to the fundamentals” approach.

Machine Learning: The Art and Science of Algorithms provides a variety of case studies with escalating complexity, multiple examples, and illustrations. In addition, the book consists of logical, geometric, statistical models along with complicated and new topics like ROC analysis and matrix factorization.

Topics covered in the book include:

• The ingredients of machine learning
• Binary classification and related tasks
• Beyond binary classification
• Rule models
• Tree models
• Linear models

12. Natural Language Processing with Python

Natural language processing is an integral part of machine learning systems. The Natural Language Processing with Python book utilizes the Python programming language to lead you to use NLTK, the popular suite of Python libraries and programs for statistical and symbolic natural language processing in English and NLP.

The book shows robust Python codes demonstrating NLP in a straightforward manner. Readers can reach well-annotated datasets to analyze and solve linguistic structures in text, unstructured data, and other NLP-oriented aspects.

The book covers:

• How human language works
• Linguistic data structures
• Parsing and semantic analysis
• Popular linguistics databases
• Natural Language Toolkit (NLTK)
• Integrate techniques from artificial intelligence and linguistics

13. The Elements of Statistical Learning

In case you love statistics and want to understand machine learning from the angle of stats, you should not miss The Elements of Statistical Learning: Data Mining, Inference, and Prediction. This reference source focuses on mathematical derivations for identifying the basic logic of a machine learning algorithm. However, remember to prepare a fundamental knowledge of linear algebra before you choose this book.

Some topics covered in the book:

• Ensemble learning
• Model inference and averaging
• Random forests
• Linear approaches for classification and regression
• High-dimensional problems
• Supervised and unsupervised learning
• Neural networks

14. Machine Learning: A Probabilistic Perspective

Packed with informal writing and pseudocode for crucial algorithms, the Machine Learning: A Probabilistic Perspective is an interesting machine learning book that expresses nostalgic color pictures and practical, hands-on examples belonging to different domains such as computer vision, robotics, biology, and text processing.

This book emphasizes a principled model-based approach. It leverages graphic models for accurately defining machine learning models.

The reference book includes:

• Conditional random fields
• Optimization
• Deep learning
• Probability
• L1 regularization

15. Python Machine Learning

Python Machine Learning is a newbie-friendly machine learning book describing the fundamentals of machine learning and its significance in the digital realm. The book covers the different branches of machine learning and its wide range of applications.

Moreover, the author also discusses the basics of Python developers programming and how to begin with the free and open-source programming language. Once you complete reading this machine learning book, you can code in Python for building a number of machine learning tasks.

The book includes:

• Fundamentals of artificial intelligence
• Basics of the Python programming language
• Logistics regression
• Decision trees
• Deep neural networks

16. Bayesian Reasoning and Machine Learning

For those who are concerned with machine learning, Bayesian Reasoning and Machine Learning is a should-have. The book is an amazing solution for computer scientists engrossed in exploring machine learning but doesn’t have a background in linear algebra and calculus.

David Barber provides a number of well-explained examples and exercises, making it suitable for undergraduate and graduate computer science students. Furthermore, the book offers additional online resources and a thorough software package that contains demos and teaching materials for instructors.

The book includes:

• Approximate interference
• Learning in probabilistic models
• Probabilistic reasoning
• The framework of graphical models
• Naive Bayes algorithm
• Dynamic models

17. Machine Learning in Action

Machine Learning in Action is considered a favorite machine learning book of many peoples, ranging from undergraduates to professionals. It compiles not only machine learning techniques but also their underlying concepts in a concise manner.

This documentation can also be a detailed instruction for ML developers to create their own meaningful programs. Plus, the author focuses on the algorithm forming the basis of different machine learning techniques.

The book consists of:

• Fundamentals of machine learning
• K-means clustering
• Tree-based regression
• Big Data and MapReduce
• Logistic regression
• FP-growth
• Support vector machines

18. Machine Learning with TensorFlow

TensorFlow is a symbolic math library. It is also listed in the leading data science Python libraries, which are used for machine learning applications. The Machine Learning with TensorFlow book provides readers a comprehensive explanation of machine learning concepts and hands-on code experience.

Moreover, the book discusses machine learning fundamentals with traditional classification, clustering, and prediction algorithms. It delves into deep learning concepts, which supports readers to implement any type of machine learning task by using the free and open-source TensorFlow library.

Topics covered in the book:

• Convolutional, recurrent, reinforcement neural networks
• Hidden Markov models
• Autoencoders
• Deep learning
• Reinforcement learning
• Linear regression

19. Understanding Machine Learning

The Understanding Machine Learning book gives a structured introduction to machine learning. The book shows the basic theories and algorithmic paradigms of machine learning and mathematical derivations.

Additionally, it provides a large number of machine learning topics in an easy-to-absorb way. The reference source is for those who range from computer science pupils to non-expert readers in computer science, mathematics, statistics, and engineering.

The book includes several topics:

• Pac-Bayes approach
• Convexity and stability
• Machine learning algorithms
• Structured output learning
• Neural networks
• Stochastic gradient descent
• The computational complexity of learning

20. Data Mining

Data mining techniques can help us explore patterns in huge data sets by approaches that belong to the database systems, statistics, and machine learning. If you have an interest in learning data mining techniques and machine learning, you can consider choosing the Data Mining: Practical Machine Learning Tools and Techniques book.

This book emphasizes the technical part of machine learning. It delves into the technical information of machine learning, methods for acquiring data, and utilizing various inputs and outputs for evaluating results.

Some topics covered in the paperback:

• Clustering
• Linear models
• Instance-based learning
• Traditional and modern data mining te
• Predicting performance
• Comparing data mining methods
• Statistical modeling
• Knowledge representation & clusters

21. The Hundred-page Machine Learning Book

The Hundred-page Machine Learning Book can demonstrate multiple machine learning topics in 100 pages only. With an easy-to-understand manner, the book is trusted by famous thought leaders such as Head of Engineering at eBay - Sujeet Varajkhedi, and the Director of Research at Google - Peter Norvig.

After reading the book thoroughly, you can create and appreciate complicated AI systems, complete an ML-based interview, and even run your ML-based business.

The book includes some topics:

• Basic algorithms
• Neural networks and deep learning
• Anatomy of a learning algorithm
• Supervised learning and unsupervised learning
• Other forms of learning

Wrapping up

We’ve shown you the 21 best machine learning books that you can go through to advance in this field. Machine learning is a top job option these days. So it’s about time for you to learn about it and make a profitable career out of it.

If you have read one of any books above, could you share your opinions with other readers by leaving some comments? We’re pleased to hear from you.