Best Machine Learning Books

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What are the best Machine Learning Books?

Machine Learning is a hot topic for IT and Data Science professionals. While the advanced implementations are within the grasp of the Machine Learning practitioner, the core concepts are easy to understand for any newbie.

Books are the best way to get started. There are books for everyone and every interest. Books can walk you through a self-paced Machine Learning curve, teach you the fundamentals, guide you in calling data, designing models, and model implementation.

In this blog, we list some of the most popular resources for machine learning and intermediate learners.

Here is a roll of some of the best books cherry-picked just for you!

For Beginners

1. Machine Learning for Absolute Beginners: A Plain English Introduction by Oliver Theobald

The book provides a practical introduction to machine learning. It covers the mathematical and statistical fundamentals of designing machine learning models. The book has easy-to-understand explanations and visual examples, making it a must-read for every Machine Learning beginner.

2. Machine Learning for Dummies by John Paul Mueller and Luca Massaron

The book is an elementary guide to Machine Learning. It gives you a hang of the programming languages and tools you need for machine learning tasks.

As a comprehensive guide, the book begins with how to get started and moves on to detailed explanations of how the underlying algorithms work. The book also covers how coding in Python and R teaches machines to find patterns and analyze results.

3. Programming Collective Intelligence by Toby Segaran

This book from O’Reilly is a practical hands-on guide for implementing machine learning. Like most O’Reilly books, it is a comprehensive how-to guide to create algorithms in machine learning for gathering data for projects. It includes sections on how to create programs to access data from websites, collect data from apps, filtering techniques, ways to make predictions, and much more.

4. Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies by John D. Kelleher, Brian Mac Namee, and Aoife D’Arcy

The book covers all the fundamentals of machine learning, from the theory side to the use of practical applications, working examples, and case studies. The book is useful for those with analytical knowledge. Each learning concept includes algorithms and models, along with working examples to showcase the concepts in practice.

5. The Hundred-Page Machine Learning Book by Andriy Burkov
This book explains all the basic concepts of Machine Learning in 100 pages. It is written in an easy-to-understand manner and is recommended by Professors and industry leaders as one of the best books to start with. The book has enough concepts to help you to clear a Machine Learning-based interview.

6. Hands-On Machine Learning with Scikit-Learn, Keras and Tensor Flow: Concepts, Tools, and Techniques to Build Intelligent Systems 2nd Edition by Aurélien Géron

The book helps you gain an understanding of the concepts and tools for building intelligent systems.

7. Machine Learning in Action by Peter Harrington

This guide walks newcomers through the techniques needed for Machine Learning and the concepts behind the practices. It is more like a tutorial as it teaches developers how to code programs to acquire data for analysis. The programming language snippets feature code and algorithm examples to get you started. Familiarity with the Python programming language is a must-have to get the best out of the book.

8. Data Mining: Practical Machine Learning Tools and Techniques by Ian H. Witten, Eibe Frank, and Mark A. Hall

The focus is on the technical details for Machine Learning and ways to gather the data you need with specific mining techniques and how to use different inputs and outputs to evaluate results. The book also discusses modernization and new software in the field. Traditional techniques are also presented alongside new research and tools.

9. Machine Learning using Python by U Dinesh Kumar and Manaranjan Pradhan.

The book provides a strong foundation in Machine Learning using Python libraries. It covers both theory and practical applications.

10. Grokking Machine Learning by Luis G. Serrano.

This is a book for visual learning, written in an easy-to-understand manner with illustrations and examples. It teaches you how to apply Machine Learning to your projects using only standard Python code.

For Intermediate Learners

1. The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani, and Jerome Friedman

The book is a favorite of Machine Learning practitioners and learners, both as an introductory and a reference handbook. It covers a broad range of topics within its conceptual framework. Topics include neural networks, random forests, and testing methods. The book encourages the reader to investigate for themselves, encouraging skills useful in machine learning.

2. Machine Learning by Tom M. Mitchell.

The book is a handbook of primary approaches to machine learning but discusses several key algorithms, example data sets, and project-oriented homework assignments from various fields.

3. Programming Collective Intelligence: Building Smart Web 2.0 Applications by Toby Segaran

A book from O’Reilly Media, this is an excellent handbook to understand Machine Learning. Although this is an early publication from 2007 and uses Python as the medium, it makes a good point of reference for implementing Machine Learning. The book elaborates on how to create Machine Learning algorithms for gathering data from applications, creating programs for accessing data from websites, and inferring the gathered data. Each chapter has exercises for extending the stated algorithms and further improving their effectiveness.

4. Pattern Recognition and Machine Learning by Christopher M. Bishop

The book is used as a university textbook. It is a reference point for anyone looking to understand the statistical techniques behind machine learning. The book also includes a test and extensive questions at the end to reinforce what you have learned.

5. Machine Learning for Hackers: Case Studies and Algorithms to Get you Started by Drew Conway and John Myles White

Another publication from O’Reilly Media, this book is for the experienced programmer interested in crunching data. Do not get misled by the word “hackers”, it refers to those who are experts in mathematics. Most of the book is based on data analysis in R and is an excellent option for those with a good knowledge of R.

The book includes e case studies highlighting the importance of using machine learning algorithms and enumerates real-life examples to make learning Machine Learning easier and faster.