10 Best Machine Learning Books for Beginners in 2023

Are you eager to begin exploring the exciting field of machine learning? If so, check out our 10 best machine learning books for beginners.

These books are packed with valuable information and resources to help you start your journey to becoming a machine learning expert.

From foundational concepts to practical techniques, these books cover various topics essential for understanding and working with machine learning algorithms.

So if you’re ready to take the first step in your machine learning journey, these books are a great place to start.

What is machine learning?

Machine learning is a subfield of artificial intelligence that involves using algorithms and statistical models to enable a system to improve its performance on a specific task without being explicitly programmed.

This ability to automatically improve performance based on experience makes machine learning a valuable tool for companies.

It can help them better understand and predict customer behavior and business trends. Machine learning is now widely used in a variety of applications, and many giant companies, such as Google. Uber, and Facebook, have integrated it into their operations.

If you are new to this technology and are looking for the 10 best machine learning books on the subject, here is a list.

Also Read5 Key Differences Between Artificial Intelligence and Machine Learning

Benefits of learning about machine learning from books

There are many free and paid online resources to learn machine learning. But, learning machine learning concepts from books has several benefits as well. Before we deep dive and explore the best ML books for beginners in 2023, let’s first see a few benefits of learning ML from books:

  • In most cases, books often provide more in-depth coverage of machine learning topics than online resources. Thus, making them a good choice for beginners to start their ML journey.
  • You can carry your favorite ML book anywhere and start learning without an internet connection.
  • Some learners prefer reading books as they offer an immersive learning experience. So, if you are one of them? Then, grab a machine learning book from the list below.
  • Some books often come with additional case studies and machine learning projects to help beginners test their skills before appearing for an interview.

Let’s now see the top machine learning books for beginners in 2023 without further ado.

Also Read11 Best Python Libraries for Machine Learning

10 Best Machine Learning Books for Beginners in 2023

best machine learning books for beginners

1. Hands-on ML with Scikit-Learn, Keras & TensorFlow by Aurelien Geron

Best For: Best for beginners to learn machine learning with exercises to apply what you will learn.

It is one of the best machine learning books that all ML enthusiasts need. The book gives a practical introduction to ML with the help of library Scikit-Learn, Keras & TensorFlow2.

Hands-on ML has two editions, but the second edition covers topics such as Biological Neurons, Supervised and Unsupervised Learning Techniques, Neural Networks, Deep Learning, Deep CV using CNN, Algorithm Fundamentals, and End-To-End Projects which will keep you hooked.

It also has some exercises to solve, which can help you apply what you have learned.

Machine learning books you will learn in this book:

  • Tensor Flow to build and train neural networks
  • Create an end-to-end machine learning project using Scikit-Learn
  • Machine learning training models such as deep reinforcement learning, support vector machines, recurrent nets, and others
  • Train and scale deep neural networks

Also Read10 Best Programming Books You Need to Read in 2023

2. The Hundred-Page Machine Learning Book by Andriy Burkov

The Hundred-Page Machine Learning Book en français (French Edition)
25 Reviews
The Hundred-Page Machine Learning Book en français (French Edition)
  • Amazon Kindle Edition
  • Burkov, Andriy (Author)
  • French (Publication Language)
  • 01/10/2022 (Publication Date) - Andriy Burkov (Publisher)

Last update on 2023-01-28 / Images from Amazon Product Advertising API

Best For: Beginners good with mathematical concepts.

If you are one of them with an understanding of statistics, probability, and maths, this could be the best for you. It touches on the varied topics across machine learning with easy-to-understand concepts. The book could be a one-stop solution for a novice in machine learning.

The important topics that the author covers in this book are Fundamental Algorithms, Deep Learning and Neural Networks, Advanced Practice, Anatomy of a Learning Algorithm, and Unsupervised Learning.

Topics Covered in this ML book:

  • What are machine learning and its various types
  • Machine learning fundamental algorithms: Linear regression, logistic regression, Decision Tree Learning, support vector machine, and k-Nearest Neighbors.
  • How machine learning engineers work
  • Model performance assessment
  • Neural networks and deep learning

Also Read10 Best Programming Books for Beginners

3. Mathematics for Machine Learning by Marc Peter Deisenroth

Best For: Grasp underlying mathematical concepts of machine learning

As you know, mathematics is a vital part of ML, and this is one of the best machine-learning books to help you with it. The book not only talks about the concepts of ML but also delivers essential mathematical skills.

Even the novice can understand and learn the concepts better, as the book has a lot of examples. Mathematics for Machine Learning introduces you to building better mathematical foundations and using, for instance, ML algorithms.

Topics Covered in this ML book:

  • Introduction and Motivation
  • Linear Algebra
  • Analytics Geometry
  • Vector calculus
  • Probability and distributions
  • Continuous optimization
  • Linear regression and Gaussian Mixture Models
  • Classification with Support Vector Machines

Also Read10 Best Books On Data Science For Beginners [2023]

4. Machine Learning for Absolute Beginners by Oliver Theobald

Sale
Machine Learning for Absolute Beginners: A Plain English Introduction (Third Edition) (Machine Learning with Python for Beginners Book 1)
180 Reviews
Machine Learning for Absolute Beginners: A Plain English Introduction (Third Edition) (Machine Learning with Python for Beginners Book 1)
  • Amazon Kindle Edition
  • Theobald, Oliver (Author)
  • English (Publication Language)
  • 181 Pages - 12/31/2020 (Publication Date) - Scatterplot Press...

Last update on 2023-01-28 / Images from Amazon Product Advertising API

Best For: People with no programming experience who want to get started with ML.

If you are a beginner looking for where to start your learning process, this book can help you gain in-depth knowledge about ML concepts. It is one of the top machine-learning books for beginners to read in 2023.

The book covers a vast arena of topics, such as Regression analysis, Machine Learning libraries, Bias/Variance topics, ML Models, and Clustering to seek new relationships.

Topics Covered in this ML book:

  • Introduction to machine learning and its applications
  • Types of machine learning algorithms
  • Preprocessing and cleaning data
  • Exploring and visualizing data
  • Model evaluation and selection
  • Regression and classification algorithms
  • Clustering algorithms
  • Deep learning and neural networks

Also Read10 Best Books To Learn Python For Beginners And Experts

5. Patterns Recognition and Machine Learning by Christopher M. Bishop

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Pattern Recognition and Machine Learning (Information Science and Statistics)
106 Reviews
Pattern Recognition and Machine Learning (Information Science and Statistics)
  • Bishop, Christopher M. (Author)
  • English (Publication Language)
  • 758 Pages - 08/23/2016 (Publication Date) - Springer (Publisher)

Last update on 2023-01-28 / Images from Amazon Product Advertising API

Best For: Comprehensive coverage and detailed explanations

Pattern recognition has evolved substantially over the past few years. It is a book that most universities recommend reading and having in their syllabus. The book has multivariate calculus and linear algebra, which might scare you.

But it can give you a great learning experience. The author reflects on the development of the subject with ample examples of machine learning and patterns in an easy-to-understand manner.

Topics Covered in this ML book:

  • Probability distributions
  • Linear models for regression
  • Neural networks and kernel methods
  • Sparse kernel machines
  • Graphical models and sampling methods

Also Read10 Best Data Science Coursera Courses For Beginners

6. Machine Learning Engineering by Andriy Burkov

Best For: Comprehensive resource for individuals who want to learn about the practical aspects of building and deploying machine learning systems.

The author has explained how to structure machine learning projects in this book in the most accessible language possible. It provides the reader with the best practices and cautions on potential challenges in this journey. The book is one of the best machine learning books for all ML enthusiasts.

Topics Covered in this ML book:

  • Data preprocessing and feature engineering
  • Model selection and evaluation
  • Machine learning pipelines and automation
  • Deployment and monitoring of machine learning models
  • Ethics and fairness in machine learning

Also Read: How To Open a File in Python

7. Deep Learning by Ian Goodfellow, Yoahua Bengio, and Aaron Courville

Best For: Learn deep learning along with machine learning concepts.

This is a comprehensive book that will elevate your understanding of deep learning. It covers mathematical concepts and deep-learning techniques, which can be great for all types of readers.

Deep Learning is one of the top machine learning books that can help you dive deeper into maths and theory to understand the subject better.

Topics Covered in this ML book:

  • Neural networks and their applications
  • Deep feedforward networks
  • Convolutional neural networks
  • Recurrent neural networks
  • Autoencoders and representation learning
  • Deep generative models

Also ReadUnderstand Python Lambda Functions With Examples

8. Speech and Language Processing by Daniel Jurafsky and James H. Martin

Speech and Language Processing, 2nd Edition
57 Reviews
Speech and Language Processing, 2nd Edition
  • Hardcover Book
  • Jurafsky, Daniel (Author)
  • English (Publication Language)
  • 1032 Pages - 05/16/2008 (Publication Date) - Prentice Hall (Publisher)

Last update on 2023-01-28 / Images from Amazon Product Advertising API

Best For: Learn the concepts of speech and language processing.

Speech and Language Processing is a book that comprehensively introduces fundamental concepts and techniques in an easy-to-understand language.

This is one of the best ML books and is widely regarded as a critical resource for students and researchers working in natural language processing and related domains.

Topics Covered in this ML book:

  • The basics of phonetics, phonology, and syntax
  • Statistical language modeling
  • Part-of-speech tagging and parsing
  • Information retrieval and extraction
  • Machine translation and summarization
  • Spoken language processing

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9. Python Machine Learning by Example by Yuxi Liu

Best For: Individuals who want to learn about machine learning using the Python programming language.

It provides hands-on experience using machine learning through Python. It covers vast topics around machine learning algorithms and techniques, along with practical examples.

It is one of the must-read and best ML books for beginners who want to learn how to apply ML techniques through Python.

Topics Covered in this ML book:

  • Introduction to machine learning and Python
  • Preprocessing and cleaning data
  • Exploring and visualizing data
  • Training and evaluating machine learning models
  • Unsupervised learning techniques
  • Deep learning with neural networks

Also ReadQuicksort Implementation in Python

10. Python for Data Analysis by Wes McKinney

Best For: Beginners who want to learn about using Python for data analysis

The book provides a hands-on introduction to data analysis using the Python programming language.

It covers a wide range of topics, including working with data in Pandas, visualizing data with Matplotlib, and performing statistical analysis with statsmodels and scikit-learn.

This is one of the best machine learning books for those new to data analysis who want to learn Python to perform these tasks.

Topics Covered in this ML book:

  • Introduction to Python and data analysis
  • Working with data in Pandas
  • Manipulating and cleaning data
  • Visualizing data with Matplotlib
  • Working with data from databases and file formats
  • Time series analysis

Summary

In summary, many excellent books are available for individuals who are new to machine learning and want to learn more about fundamental concepts and techniques.

Some of the best books for beginners in this field include “Machine Learning for Absolute Beginners” by Oliver Theobald, “Python Machine Learning by Example” by Yuxi Liu, “Deep Learning” by Ian Goodfellow, Yoahua Bengio and Aaron Courville, “Python for Data Analysis” by Wes McKinney, and “Speech and Language Processing” by Daniel Jurafsky and James H. Martin.

These books provide a wide range of information and resources for those just starting in machine learning and can be valuable for further study and exploration in this field.

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