10 Best Machine Learning Books to Ace Machine Learning in 2022

Words are the artillery of books. Every phrase encourages you to expand your imagination, which leads to increased learning. Study whenever and wherever you want, at your own pace and convenience. Are you trying to figure out which book to read to learn more about Machine Learning?

There’s no such thing as a one-size-fits-all book. That’s why we’ve scoured the shelves for the top books on machine learning, spanning from total beginners to skilled programmers.

What is Machine Learning?

To put it simply, machine learning is the process of learning your computer anything. It might be used to distinguish between a cat and a dog or between fruits, to detect cancer in people, or to construct a chatbot that assists someone suffering from despair.

It may be teaching your machine to read; all of this is made possible by Machine Learning. With all of that out of the picture, let’s look at all of the top books around to study Machine Learning!

Best Machine Learning Books in 2022

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

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

Author: Aurelien Geron
Publisher: O’ Reilley
Edition: 2nd
Available in: Kindle, Paperback, Spiral-bound

About the author

Aurélien Géron is a machine learning consultant and educator. As a former Googler, he supervised YouTube’s video classification team from 2013 to 2016.

About the book

Deep learning has lately improved the whole field of machine learning as a result of a series of breakthroughs. Even programmers with minimal expertise in this sector may now construct data-driven systems using simple, rapid technologies. This straightforward instruction will show you how to accomplish it.

The author helps you obtain an intuitive grasp of the principles and tools for developing intelligent systems by using simple examples, little theory, and two production-ready Python structures and Tensor Flow. From simple linear regression to deep learning models, you’ll study a variety of strategies.

To get started, you only need programming skills and the exercises in each section to help you implement what you’ve learned.

What you’ll learn

  • Investigate the landscape of machine learning, particularly neural networks.
  • Track an example machine-learning project from start to finish with Scikit-Learn
  • Investigate various training models, such as support vector machines, random forests, decision trees, and ensemble approaches.
  • Use the Tensor Flow library to build and train neural networks.
  • Examine neural net architectures include convolutional networks, recurrent nets, and deep reinforcement learning.
  • Discover how to build and scale deep neural networks.

You can buy this book from here.

2. The Hundred-Page Machine Learning Book

The Hundred-Page Machine Learning Book

Author: Andryiv Burkov
Publisher: Andryiv Burkov
Edition: 1st
Available in: Kindle, Paperback

About the author

Andriy Burkov is a father of two who works as a machine learning expert in Quebec City, Canada. He earned his Ph.D. in Artificial Intelligence 11 years ago and has been managing a team of machine learning engineers at Gartner for the previous eight years.

About the book

This is the only book you’ll ever need to understand machine learning ideas. I can certainly declare, after only a few chapters, that this is the book I’ve been looking for!

The book is easy to read and understand, and it goes through basic mathematical concepts, machine learning concepts, and the most significant fundamentals in the area.

This is a book that will last a long time. Mostly, ML books are about technology and become outdated a few years or even months after they are published. Andriy’s method goes into the fundamentals while also demonstrating how to comprehend future advancements in the subject. This is something you won’t be losing out on.

What you’ll learn

  • Notations and definitions
  • Fundamental algorithm
  • Anatomy of a learning algorithm
  • Neural networks and deep learning
  • Problems and solutions

You can buy this book from here.

3. Python Machine Learning

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

Author: Sebastian Rascha
Publisher: Packt publishing
Edition: 3rd
Available in: Kindle, Paperback

About the author

Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison, where he specializes in machine learning and deep learning. His current research has concentrated on broad issues such as few-shot learning for working with little data and creating deep neural networks for ordinal purposes.

About the book

Python Machine Learning provides you with a thorough introduction to Python machine learning and deep learning. It serves as a step-by-step lesson as well as a reference that you’ll resort to when you construct your machine learning systems.

The book covers all of the fundamental machine learning techniques in detail, with clear explanations, graphics, and working examples. While some publications just teach you how to follow instructions, Raschka and Mirjalili’s machine learning book teaches you how to develop your own models and applications.

What you’ll learn

  • Understand the frameworks, models, and approaches that allow machines to ‘learn’ from data.
  • For machine learning, use sci-kit-learn, and for deep learning, use TensorFlow.
  • Machine learning may be used for picture categorization, sentiment analysis, intelligent online applications, and other tasks.
  • Create and tune neural networks, GANs, and other models
  • Learn about the best procedures for reviewing and tuning models
  • Using regression analysis, predict continuous desired outcomes

You can buy this book from here.

4. Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning (Information Science and Statistics)

Author: Christopher M. Bishop
Publisher: Springer
Edition: 2nd
Available in: Kindle, Paperback

About the author

Christopher Michael Bishop (born 7 April 1959) FREng, FRSE is the Laboratory Director of Microsoft Research Cambridge, a professor of computer science at the University of Edinburgh, and a Darwin College, Cambridge Fellow. Chris earned a Bachelor of Arts in Physics from St Catherine’s College, Oxford, and a PhD in Theoretical Physics from the University of Edinburgh, where he wrote his thesis on quantum field theory.

About the book

The Bayesian viewpoint is presented for the first time in a textbook on pattern recognition. In cases where exact solutions aren’t possible, the book introduces approximate inference methods that allow for quick approximate replies. When no other book applies graphical models to machine learning, it uses them to describe probability distributions.

It is not assumed that you know anything about pattern recognition or machine learning. Working knowledge of multivariate calculus and basic linear algebra is necessary, as is some prior experience with probabilities (though this is not required because the book contains a self-contained introduction to basic probability theory).

What you’ll learn

  • Model selection
  • Information theory
  • Curse of dimensionality
  • Probability distribution
  • Linear models for regression
  • Neural networks
  • Kernel methods
  • Graphical methods and more

You can buy this book from here.

5. The Elements of Statistical Learning

The Elements of Statistical Learning Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

Author: Trevor Hastle, Robert Tibshirani
Publisher: Springer
Edition: 2nd
Available in: Kindle, Paperback

About the author

Robert Tibshirani (born July 10, 1956) is a Professor of Statistics and Health Research and Policy at Stanford University. From 1985 until 1998, he was a professor at the University of Toronto. In his work, he creates statistical techniques for analyzing large datasets, most notably in genomes and proteomics.

About the book

In a shared conceptual framework, this book presents important ideas in a number of professions such as medical, biology, finance, and marketing. Despite the statistical approach, the focus is on concepts rather than mathematics. A lot of examples are offered, including a lot of color graphics. It’s a great resource for statisticians and anyone else interested in data mining in science or business.

The scope of the book is extensive, ranging from supervised (prediction) to unsupervised learning. Neural networks, support vector machines, classification trees, and boosting are among the numerous subjects covered in this book, which is the first complete study of the subject in any book.

Graphical models, random forests, ensemble approaches, least angle regression & route algorithms for the lasso, non-negative matrix factorization and spectral clustering are among the topics addressed in this significant new edition, which includes many topics not covered in the original.

What you’ll learn

  • Overview of supervised learning
  • Linear methods for regressions
  • Linear methods for classification
  • Basis expansions and regularizations
  • Kernel smoothing methods

You can buy this book from here.

6. Machine Learning for Hackers

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

Author: Drew Convey
Publisher: O’Reilly
Edition: 1st
Available in: Kindle, Paperback

About the author

Drew Conway is a Ph.D. candidate in Politics at New York University. He investigates international relations, war, and terrorism utilizing mathematical, statistical, and computer science methods to achieve a better understanding of these events. His scholarly curiosity stems from his experience as an analyst in US intelligence and defense organizations.

John Myles White is a Princeton Ph.D. candidate in Psychology. Using behavioral approaches and fMRI, he investigates pattern identification, decision-making, and economic behavior. He is particularly interested in value assessment anomalies.

About the book

This book will get you started with machine learning, a toolset of methods that allows computers to train themselves to perform useful tasks. If you’re an experienced programmer fascinated by crunching data, this book will get you started. Instead of a usual math-heavy approach, authors Drew Conway and John Myles White use a series of hands-on case studies to explain machine learning and statistical methods.

Each chapter focuses on a particular machine learning task, such as classification, prediction, optimization, or recommendation. You’ll learn how to evaluate sample datasets and create simple machine learning algorithms with the R programming language. Machine Learning for Hackers is appropriate for programmers from a variety of fields, including business, governmental, and academic research.

What you’ll learn

  • Create a simple Bayesian classifier to assess whether an email is spam based just on its text.
  • Estimate the frequency of page hits for the top 1,000 websites using linear regression.
  • By attempting to break a basic letter cipher, you might learn optimization strategies.
  • Statistically compare and contrast U.S. Senators based on their voting histories
  • Create a recommendation system for “whom to follow” based on Twitter data.

You can buy this book from here.

7. Machine Learning for Absolute Beginners: A Plain English Introduction

Machine Learning For Absolute Beginners A Plain English Introduction (Second Edition) (Machine Learning From Scratch Book 1)

Author: O Theobalt
Publisher: Scatterplot press
Edition: 3rd
Available in: Kindle, Paperback

About the book

The second edition of Machine Learning for Absolute Novices was prepared and constructed with absolute beginners in mind. This means no coding experience is required and instructions are written in plain English.

Clear explanations and visual examples are given where basic methods are explained, making it easy and exciting to follow along at home. Cross-validation, ensemble modeling, feature engineering, grid search, and one-hot encoding are just a few of the subjects covered in this important new edition.

Please keep in mind that this book is not a sequel to the First Edition, but rather a rebuilt and reworked version of it.

What you’ll learn

  • Techniques for cleaning data, such as one-shot encoding, binning, and coping with missing data
  • Gathering data for processing, including k-fold validation
  • Regression analysis to generate trend lines
  • Clustering, especially k-means clustering, to discover new linkages
  • Neural Network Fundamentals
  • Bias/Variance to enhance your machine learning model
  • Decoding categorization with Decision Trees
  • How to Create Your First Machine Learning Model to Predict House Values in Python

You can buy this book from here.

8. Machine Learning For Dummies

Machine Learning For Dummies

Author: John Paul Mueller
Publisher: For dummies
Edition: 2nd
Available in: Kindle, Paperback

Unlike some other machine learning publications, the fully up to date 2nd Edition of Machine Learning For Dummies doesn’t presume you have decades of work expertise with languages like python, but instead starts from the beginning, covering the fundamentals that will get you back in operation establishing the models you’ll need to complete practical tasks.

It examines the underlying—and fascinating—mathematical ideas that underpin machine learning, but it also demonstrates that you don’t have to be a math wiz to create entertaining new tools and use them in your job and studies.

What you’ll learn

  • Understand the history of AI and machine learning
  • Work with Python 3.8 and TensorFlow 2.x (and R as a download)
  • Build and test your own models
  • Use the latest datasets, rather than the worn-out data found in other books
  • Apply machine learning to real problems

You can buy this book from here.

9. Mathematics for Machine Learning

Mathematics for Machine Learning

Author: Marc Peter
Publisher: Cambridge University Press
Edition: 2nd
Available in: Kindle, Paperback

About the book

This self-contained textbook fills the gap between mathematics and machine learning books by presenting mathematical principles with the fewest prerequisites possible. Gaussian mixture models, principal component analysis, Linear regression, and support vector machines are four major machine learning algorithms that are derived from these notions.

These derivations provide an introduction to machine learning texts for undergraduates and those with numerical skills. The strategies aid in the development of understanding and practical knowledge of mathematical concepts for people learning mathematics for the first time.

Worked examples and activities are included in each chapter to aid comprehension. On the book’s website, you’ll find programming tutorials.

You can buy this book from here.

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

Introduction to Machine Learning with Python A Guide for Data Scientists

Author: Andreas Müller and Sarah Guido
Publisher: O’Reilly
Edition: 1st
Available in: Kindle, Paperback

About the author

Andreas Müller earned a doctorate in machine learning from the University of Bonn. He just joined the Center for Data Science at New York University after a year as a machine learning researcher on computer vision applications at Amazon.

In the last four years, he has been the maintainer and a core contributor to sci-kit-learn, a machine learning toolkit extensively used in business and academia, as well as the author and contributor to a number of other widely used machine learning packages.

About the book

Machine learning has become an essential component of many commercial uses and research efforts, but it is not limited to huge corporations with vast research teams. This book will show you practical techniques to develop your own machine learning solutions if you use Python, even if you are a newbie. Machine learning uses are only restricted by your creativity with all of the data available today.

You’ll learn how to use Python and the sci-kit-learn library to build a successful machine-learning application. The authors concentrate on the practical implications of employing machine learning algorithms instead of the mathematics underlying them. Knowledge of the NumPy and matplotlib libraries will assist you to get the most out of this book.

What you’ll learn

  • Machine learning fundamentals and applications
  • The benefits and drawbacks of widely used machine learning algorithms
  • How to portray machine learning-processed data, including which data features to emphasize
  • Advanced model evaluation and parameter tweaking techniques
  • The pipeline idea for chaining models and encapsulating your process
  • Text data processing methods, including text-specific processing approaches

You can buy this book from here.

Conclusion

That effectively sums up our suggestions for you, which range from the most fundamental to the most complicated topics. We hope you enjoy our suggestions for the best machine learning books. Look for other articles in this sequence that will cover various areas of Data Science, such as data science books, python books, and R books.

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