Best Machine learning Books


Machine learning is a term closely associated with data science. It refers to a broad class of methods that revolve around data modeling to algorithmically make predictions, and algorithmically decipher patterns in data. Machine Learning is the field of scientific study that concentrates on induction algorithms and on other algorithms that can be said to "learn".

Learning objective of these books is to know about machine learning and these books are designed for those who aspire to learn about Machine learning in depth. Here you will find 10 best Machine learning books to learn Machine learning from beginner level to advance level.

Top 10 Machine learning Books

1. Hands–On Machine Learning with Scikit–Learn and TensorFlow

Author :- Aurelien Geron
Edition :- 2nd Edition
Published by :- O'Reilly

You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started.
Explore the machine learning landscape, particularly neural nets.Use scikit-learn to track an example machine-learning project end-to-end. Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods. Use the TensorFlow library to build and train neural nets. Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning

2. Pattern Recognition and Machine Learning

Author :- Christopher M. Bishop
Edition :- 2010 Edition
Published by :- Springer

This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible.
It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed.

3. The Hundred-Page Machine Learning Book

Author :- Andriy Burkov
Edition :- 2019 Edition
Published by :- Notion Press

Burkov has undertaken a very useful but impossibly hard task in reducing all of 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 actually 150) pages you will read, not the last, provides a solid introduction to the field.
The breadth of topics the book covers is amazing for just 100 pages (plus few bonus pages!). Burkov doesn't hesitate to go into the math equations: that's one thing that short books usually drop. We really liked how the author explains the core concepts in just a few words. The book can be very useful for newcomers in the field, as well as for old-timers who can gain from such a broad view of the field.

4. Machine Learning For Absolute Beginners

Author :- Oliver Theobald
Edition :- 2nd Edition
Published by :- Independently Published

Machine Learning for Absolute Beginners Second Edition has been written and designed for absolute beginners. This means plain-English explanations and no coding experience required. Where core algorithms are introduced, clear explanations and visual examples are added to make it easy and engaging to follow along at home.
This major new edition features many topics not covered in the First Edition, including Cross Validation, Data Scrubbing and Ensemble Modeling. Please note that this book is not a sequel to the First Edition, but rather a restructured and revamped version of the First Edition. Readers of the First Edition should not feel compelled to purchase this Second Edition.

5. Understanding Machine Learning: From Theory to Algorithms

Author :- Shai Shalev-Shwartz, Shai Ben-David
Edition :- 2014 Edition
Published by :- Cambridge University Press

The first goal of this book is to provide a rigorous, yet easy-to-follow, introduction to the main concepts underlying machine learning. The second goal of this book is to present several key machine learning algorithms.
The book is divided into four parts.The first three parts of the book are intended for first-year graduate students in computer science, engineering, mathematics, or statistics. It can also be accessible to undergraduate students with the adequate background. The more advanced chapters can be used by researchers intending to gather a deeper theoretical understanding.

6. Machine Learning using Python

Author :- U Dinesh Kumar Manaranjan Pradhan
Edition :- 2019 Edition
Published by :- Wiley

This book is written to provide a strong foundation in machine learning using Python libraries by providing real-life case studies and examples. It covers topics such as foundations of machine learning, introduction to Python, descriptive analytics and predictive analytics. Advanced machine learning concepts such as decision tree learning, random forest, boosting, recommended systems, and text analytics are covered.
The book consists of 10 chapters. The sequence of the chapters is designed to create strong foundation for the learners. The first few chapters provide the foundations in Python and ML and the later chapters build on the concepts learnt in the previous chapters. We suggest readers to read the chapters in sequence for a structured learning.

7. Machine Learning – A Probabilistic Perspective

Author :- Kevin P. Murphy, Francis Bach
Edition :- 2012 Edition
Published by :- MIT Press

This book is suitabk for upper-level undergraduate students and beginning graduate students in computer science, statistics, electrical engineering, econometrics, or anyone eke who has the appropriate mathematical background Specifically, the reader is assumed to already be familiar with basic mulvarute calculus, probability, linear algebra, and computer programming, Prior exposure to statistics is helpful but not necessary.

8. Machine Learning for Hackers: Case Studies and Algorithms to Get You Started

Author :- Drew Conway, John Myles White
Edition :- 2012 Edition
Published by :- O'Reilly Media

If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning—a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.
Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research.

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

Author :- Andreas C. Müller, Sarah Guido
Edition :- 2017 Edition
Published by :- O'Reilly Media

This book is for current and aspiring machine learning practitioners looking to implement solutions to real-world machine learning problems. This is an introductory book requiring no previous knowledge of machine learning or artificial intelligence (AI).
The book focus on using Python and the scikit-learn library, and work through all the steps to create a successful machine learning application. The methods we introduce will be helpful for scientists and researchers, as well as data scientists working on commercial applications. You will get the most out of the book if you are somewhat familiar with Python and the NumPy and matplotlib libraries.

10. Machine Learning in Action

Author :-Peter Harrington
Edition :- 2012 Edition
Published by :- Dreamtech Press

“Machine Learning in Action is a unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. In it, you will use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification. As you work through the numerous examples, you will explore key topics like classification, numeric prediction, and clustering.

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