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20 Best Machine Learning Books for Beginner & Experts in 2022
Machine learning has bestowed humanity the power to run tasks in an automated manner. It allows improving things that we already do by studying a continuous stream of data related to that same task. Machine learning has a wide array of applications that belongs to different fields, ranging from space research to digital marketing.
Machine learning also forms the basis of artificial intelligence. We’re not yet flooded with machines capable of throwing judgments on their own. It’s still a long way to reach there. But the possibilities generated along the way are endless.
20 Best Machine Learning Books
So, it is the best time to pick up and learn machine learning. Of course, machine learning is a complex field but that doesn’t mean that it can’t be learned in an easy way. To help you through, here we are with our pick of the 20 best machine learning books:
Is it possible to explain various machine learning topics in a mere 100 pages? The Hundred-Page Machine Learning Book by Andriy Burkov is an effort to realize the same. Written in an easy-to-comprehend manner, the machine learning book is endorsed by reputed thought leaders to the likes of the Director of Research at Google, Peter Norvig and Sujeet Varakhedi, Head of Engineering at eBay. It is the best books for Machine Learning to start with.
Post a thorough reading of the book, you will be able to build and appreciate complex AI systems, clear an ML-based interview, and even start your very own ml-based business. The book, however, is not meant for absolute machine learning beginners. If you’re looking for something more fundamental look somewhere else.
- Anatomy of a learning algorithm
- Fundamental algorithms
- Neural networks and deep learning
- Other forms of learning
- Supervised learning and unsupervised learning
Regarded among the best books to begin understanding machine learning, the Programming Collective Intelligence by Toby Segaran was written way before, in 2007, data science and machine learning reached its present status of top career avenues. The book makes use of Python as the vehicle of delivering the knowledge to its readers.
The Programming Collective Intelligence is less of an introduction to machine learning and more of a guide for implementing ml. The book details on creating efficient ml algorithms for gathering data from applications, creating programs for accessing data from websites, and inferring the gathered data. Each chapter features exercises for extending the stated algorithms and further improving their efficiency and effectiveness.
- Bayesian filtering
- Collaborative filtering techniques
- Evolving intelligence for problem-solving
- Methods for detecting groups or patterns
- Non-negative matrix factorization
- Search engine algorithms
- Support vector machines
- Ways to make predictions
The Machine Learning for Hackers book is meant for the experienced programmer interested in crunching data. Here, the word hackers refer to adroit mathematicians. As most of the book is based on data analysis in R, it is an excellent option for those with a good knowledge of R. The book also details using advanced R in data wrangling.
Perhaps the most important highlight of the Machine Learning for Hackers book is the inclusion of apposite case studies highlighting the importance of using machine learning algorithms. Rather than delving deeper into the mathematical theory of machine learning, the book explains numerous real-life examples to make learning ml easier and faster.
- Developing a naïve Bayesian classifier
- Linear regression
- Optimization techniques
- Using R for querying data
Machine Learning by Tom M. Mitchell is a fitting book for getting started with machine learning. It offers a comprehensive overview of machine learning theorems with pseudocode summaries of the respective algorithms. The Machine Learning book is full of examples and case studies to ease a reader’s effort for learning and grasping ml algorithms.
If you wish to start your career in machine learning, then this book is a must-have. Thanks to a well-explained narrative, a thorough explanation of ml basics, and project-oriented homework assignments, the book on machine learning is a suitable candidate to be included in any machine learning course or program.
- Genetic algorithms
- Inductive logic programming
- Introduction to primary approaches to machine learning
- Machine learning concepts and techniques
- Re-enforcement learning
If you like statistics and want to learn machine learning from the perspective of stats then The Elements of Statistical Learning is the book that you must read. The machine learning book emphasizes mathematical derivations for defining the underlying logic of an ml algorithm. Before picking up this book, ensure that you have at least a basic understanding of linear algebra.
The concepts explained in The Elements of Statistical Learning book aren’t beginner-friendly. Hence, you might find it complex to digest. If you still, however, want to learn them then you can check out the An Introduction to Statistical Learning book. It explains the same concepts but in a beginner-friendly way.
- Ensemble learning
- High-dimensional problems
- Linear methods for classification and regression
- Model inference and averaging
- Neural networks
- Random forests
- Supervised and unsupervised learning
Want to get a comprehensive introduction to machine learning in less time? And have a good understanding of engineering mathematics? Try the Learning from Data: A Short Coursebook. Instead of imparting knowledge about the various advanced concepts pertaining to machine learning, the book prepares its readers to better comprehend the complex machine learning concepts.
The Learning from Data: A Short Coursebook ditches lengthy and beating around the bush explanations for succinct, to the points explanations. To reinforce learning from this machine learning book, you can also refer to the online tutorials from the author Yaser Abu Mostafa.
- Error and Noise
- Kernel methods
- Radial basis functions
- Support vector machines
Written by Christopher M. Bishop, the Pattern Recognition and Machine Learning book serves as an excellent reference for understanding and using statistical techniques in machine learning and pattern recognition. A sound understanding of linear algebra and multivariate calculus are prerequisites for going through the machine learning book.
The Pattern Recognition and Machine Learning book present detailed practice exercises for offering a comprehensive introduction to statistical pattern recognition techniques. The book leverages graphical models in a unique way of describing probability distributions. Though not mandatory, some experience with probability will hasten the learning process.
- Approximate inference algorithms
- Bayesian methods
- Introduction to basic probability theory
- Introduction to pattern recognition and machine learning
- New models based on kernels
Natural language processing is the backbone of machine learning systems. The Natural Language Processing with Python book uses the Python programming language to guide you into using NLTK, the popular suite of Python libraries and programs for symbolic and statistical natural language processing for English and NLP in general.
The Natural Language Processing with Python book presents powerful Python codes demonstrating NLP in a clear, precise manner. Readers are able to access well-annotated datasets for analyzing and dealing with unstructured data, linguistic structure in text, and other NLP-oriented aspects.
- How human language works
- Integrate techniques from artificial intelligence and linguistics
- Linguistic data structures
- Natural Language Toolkit (NLTK)
- Parsing and semantic analysis
- Popular linguistic databases
For anyone interested in entering the field of machine learning, Bayesian Reasoning and Machine Learning is a must-have. The book is a fitting solution for computer scientists interested in learning ml but doesn’t have a background in calculus and linear algebra.
There is no scarcity of well-explained examples and exercises in the Bayesian Reasoning and Machine Learning book. This makes the book also ideal for undergraduate and graduate computer science students. The machine learning book comes with additional online resources and a comprehensive software package that includes demos and teaching materials for instructors.
- Approximate interference
- Dynamic models
- The framework of graphical models
- Learning in probabilistic models
- Naive Bayes algorithm
- Probabilistic reasoning
The Understanding Machine Learning book offers a structured introduction to machine learning. The book dives into the fundamental theories and algorithmic paradigms of machine learning, and mathematical derivations.
The machine learning presents a wide array of machine learning topics in an easy-to-understand way. The Understanding Machine Learning book is fitting for anyone ranging from computer science students to non-expert readers in computer science, engineering, mathematics, and statistics.
- The computational complexity of learning
- Convexity and stability
- Neural networks
- ML algorithms
- PAC-Bayes approach
- Stochastic gradient descent
- Structured output learning
Have no prior experience and exposure to machine learning? But still, want to learn it? Then you must not miss out on the Machine Learning for Absolute Beginners book by Oliver Theobald. Obviously, no coding or mathematical background is required to benefit from this machine learning book.
For anyone looking to get the most toned-down definition of machine learning and related concepts, the Machine Learning for Absolute Beginners book is one of the most fitting options. In order to ensure that the readers follow everything mentioned in the book easily, clear explanations and visual examples accompany various ml algorithms.
- Basics of neural networks
- Data scrubbing techniques
- Ensemble modeling
- Feature engineering
- Regression analysis
The Machine Learning for Dummies book aims to make the readers familiar with the basic concepts and theories pertaining to machine learning in an easy way. Also, the book focuses on the practical, real-world applications of machine learning.
The machine learning book from John Paul Mueller and Luca Massaron uses Python and R code to demonstrate how to train machines to find patterns and analyze results. The book also explains how ml facilitates email filters, fraud detection, internet ads, web searches, etc.
- Data preparation
- Machine learning techniques
- Supervised and unsupervised learning
- The machine learning cycle
- Training machine learning systems
- Tying machine learning methods to outcomes
13. Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies
Predictive analytics makes use of an array of statistical techniques that helps in analyzing the past and current events to make future predictions based on the same. The Fundamentals of Machine Learning for Predictive Data Analytics book dives into the basics of machine learning required to do better predictive data analytics.
Obviously, you need to have at least a sound understanding of the basics of predictive data analytics to benefit from the machine learning book. Each machine learning concept explained in the machine learning book comes with suitable algorithms, models, and well-explained examples.
- Error-based learning
- Information-based learning
- Probability-based learning
- Similarity-based learning
- Techniques for evaluating prediction models
The Machine Learning in Action is yet another opportune machine learning book preferred by a variety of people ranging from undergraduates to professionals. It not only details machine learning techniques but the concepts underlying them as well as in a thoroughly-explained way.
The machine learning book can also act as a walkthrough for developers for writing their own programs meant for acquiring data with the aim of analysis. The Machine Learning in Action book goes in-depth in discussing the algorithms forming the basis of various machine learning techniques. Most examples mentioned in the machine learning book use Python code.
- Basics of machine learning
- Big Data and MapReduce
- K-means clustering
- Logistic regression
- Support vector machines
- Tree-based regression
Data mining techniques help us discover patterns in large data sets by means of methods that belong to the fields of database systems, machine learning, and statistics. If you need to or plan to learn data mining techniques, in particular, and machine learning, in general then you must pick up the Data Mining: Practical Machine Learning Tools and Techniques book.
The top machine learning book focuses more on the technical aspect of machine learning. It dives deeper into the technical details of machine learning, methods for obtaining data, and using different inputs and outputs for evaluating results.
- Comparing data mining methods
- Instance-based learning
- Knowledge representation & clusters
- Linear models
- Predicting performance
- Statistical modeling
- Traditional and modern data mining techniques
TensorFlow is a symbolic math library, and one of the top data science Python libraries, that is used for machine learning applications, most notably neural networks. The Machine Learning with TensorFlow book offers readers a robust explanation of machine learning concepts and practical coding experience.
The Machine Learning with TensorFlow book explains the ml basics with traditional classification, clustering, and prediction algorithms. The book all dives deeper into deep learning concepts making the readers ready for any kind of machine learning task using the free and open-source TensorFlow library.
- Convolutional, recurrent, reinforcement neural networks
- Deep learning
- Hidden Markov models
- Linear regression
- Reinforcement learning
17. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
The second edition of the Hands-On Machine Learning adds Keras to its content list, alongside Scikit-Learn and TensorFlow. The machine learning book gives an intuitive understanding of the various concepts and tools that you need to develop smart, intelligent systems.
You need programming experience to get started with the Hands-On Machine Learning book. Each chapter in the machine learning book features numerous exercises that will help you apply what you’ve learned till that time. Post successful reading of the book, one should be able to implement intelligent programs capable of learning from data gained.
- Deep neural networks
- Deep reinforcement learning
- Linear regression
- Training models, including decision trees, ensemble methods, random forests, and support vector machines
- Training neural nets
Are you a data scientist proficient in using Python and interested in learning ML? Then the Introduction to Machine Learning with Python: A Guide for Data Scientists is the ideal book for you to pick up and kickstart your machine learning journey.
The Introduction to Machine Learning with Python: A Guide for Data Scientists book will teach you various practical ways of building your very own machine learning solutions.
You will get to know all the important steps for creating robust machine learning applications using Python and Scikit-learn library. Having a good understanding of matplotlib and NumPy libraries will help the learning process even better.
- Advanced methods for model evaluation and parameter tuning
- Applications, fundamental concepts of machine learning
- Machine learning algorithms
- Methods for working with text data
- Pipelines for chaining models and encapsulating workflow
- Representation of processed data
19. Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)
Full of informal writing and pseudocode for important algorithms, the Machine Learning: A Probabilistic Perspective is a fun machine learning book that flaunts nostalgic color images and practical, real-world examples belonging to various domains like biology, computer vision, robotics, and text processing.
Unlike other machine learning books that are written like a cookbook explaining several heuristic methods, Machine Learning: A Probabilistic Perspective focuses on a principled model-based approach. It uses graphical models for specifying ml models in a concise, intuitive way.
- Conditional random fields
- Deep learning
- L1 regularization
A beginner-friendly machine learning book, the Python Machine Learning book details the basics of machine learning as well as its importance in the digital sphere. The book also discusses the various branches of machine learning and its wide variety of applications.
The Python Machine Learning book also details the fundamentals of Python programming and how to get started with the free and open-source programming language. Post the successful completion of the machine learning book, you will be able to code in Python to successfully establish a wide variety of machine learning tasks.
- Basics of artificial intelligence
- Decision trees
- Deep neural networks
- Fundamentals of the Python programming language
- Logistic regression
Some Other Top Machine Learning Books
Other than the top 20 machine learning books that we have enumerated already, here is a list of some other great machine learning and related books:
- Advances in Financial Machine Learning by Marcos Lopez de Prado
- A Brief Introduction to Neural Networks by David Kriesel
- A Programmer’s Guide to Data Mining by Ron Zacharski
- An Introduction to Statistical Learning: With Applications in R by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
- Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville
- Deep Learning with Python by Francois Chollet
- Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms by Nicholas Locascio and Nikhil Buduma
- Machine Learning: A Bayesian and Optimization Perspective by Sergios Theodoridis
- Machine Learning: An Algorithmic Perspective by Stephen Marsland
- Machine Learning: The Art and Science of Algorithms that Make Sense of Data by Peter A. Flach
- Machine Learning: The Ultimate Beginners Guide For Neural Networks, Algorithms, Random Forests, and Decision Trees Made Simple by Ryan Roberts
- Machine Learning with R: Expert Techniques for Predictive Modeling by Brett Lantz
- Machine Learning Yearning by Andrew Ng
- Mining of Massive Datasets by Anand Rajaraman and Jeffrey David Ullman
- Neural Networks and Deep Learning by Pat Nakamoto
- Probabilistic Graphical Models: Principles and Techniques by Daphne Koller and Nir Friedman
- Python Machine Learning: Machine Learning and Deep Learning with Python, Scikit-learn, and TensorFlow by Sebastian Raschka and Vahid Mirjalili
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie
- Think Stats – Probability, and Statistics for Programmers by Allan B. Downey
- Understanding Machine Learning: From Theory to Algorithms by Shai Ben-David and Shai Shalev-Shwartz
That sums up the 20 best machine learning books that you can go through to advance in machine learning the way you want it. Other than reading books, you can also gain machine learning knowledge by means of the best machine learning tutorials, YouTube videos, online courses, and whatnot!
Machine learning is a hot career option these days. The future looks all bright and shiny for it. So, it is high time to jump into the scene and make a profitable, professional career out of it.
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