7 Best Machine Learning Books for Beginners in 2023

Have you had to verify yourself as a human by marking the images with a stop sign? You are also engaging in an action that helps machines identify objects in images.

Have you had to verify yourself as a human by marking the images with a stop sign? You are also engaging in an action that helps machines identify objects in images.

Machine learning (ML) is truly a shining star of the moment. It is an application of Artificial Intelligence that allows software applications to become accurate in predicting outcomes.

There are so many great Machine Learning books out there for beginners. Choosing the right book is important as it can guide your learning. In this article, we will cover the top 7 books on Machine Learning for beginners.


Why Learn Machine Learning?

Everyone has a different take on machine learning. We have rounded up the top reasons why you should learn Machine Learning:

  • Better career opportunities and growth: If you are looking to take your career to another level, Machine Learning is for you. Becoming an expert in ML makes an individual a hot commodity to most of the employers.

  • Machine Learning experts earn a pretty penny: If your goal is to earn money and get a good salary in the coming years, then Machine Learning becomes more vital. It will definitely have a significant impact on your salary.

  • Machine learning is linked directly to Data Science: Machine Learning appears as a shadow of data science. Machine Learning career endows you with two hats, one is for a machine learning engineer job and the other is for a data scientist job. You can learn more about the best books for data scientists.

  • Active community: Machine Learning has a strong and thriving community support. You will find great learning resources online to polish your skills.

  • Used by popular companies: Machine Learning is used by popular companies like PayPal, Walmart, GoDaddy, Flickr, Storylens, and IBM.


What Makes The Best Machine Learning Books? 

Here are our criteria for selection of the books:

  • The book should contain a variety of instructional materials, including exercises, examples, questions, learning activities, and other features that promote a programmer’s engagement and active learning.

  • It uses clear, precise, and easy-to-understand language.

  • Content must be up-to-date and should thoroughly teach and explain the basic concepts of Machine Learning.

  • Contain programming assignments for practice and hands-on experience

  • Strictly focus on machine learning.

  • The book should have a clear layout and must be friendly toward self-taught programmers.


Best Books on Machine Learning

Now that you are aware of all the reasons to learn Machine Learning, here are some of the best books you should consider to learn it.

Let’s look at them in brief:


1. Best Book for Absolute Beginners: Machine Learning For Absolute Beginners

Machine Learning for Absolute Beginners: A Plain English Introduction by Oliver Theobald provides a practical and high-level introduction to machine learning. It focuses on the high-level fundamentals of machine learning and it is free with Kindle Unlimited.

The book covers the mathematical and statistical underpinnings of designing machine learning models. It includes clear explanations and visual examples to make it easy and engaging to follow along at home.

The book is divided into seventeen chapters and includes the following contents:

  • Chapter 1 gives you the introduction

  • Chapter 2 explores the Machine Learning

  • Chapter 3 covers Ml Categories

  • Chapter 4 covers the Ml Toolbox

  • Chapter 5 covers Data Scrubbing

  • Chapter 6 talks about setting up your data

  • Chapter 7 covers Regression Analysis

  • Chapter 8 covers Clustering

  • Chapter 9 talks about Bias & Variance

  • Chapter 10 covers Artificial Neural Networks

  • Chapter 11 talks about Decision Trees

  • Chapter 12 covers Ensemble Modeling

  • Chapter 13 talks about building a model in Python

  • Chapter 14 covers Model Optimization

  • Chapter 15 discuss further resources

  • Chapter 16 talks about downloading Datasets

  • Chapter 17 gives you the final word

This book is designed for absolute beginners, so no prior programming experience is required.


2. Best Quick-Start Guide: Machine Learning For Dummies

Machine Learning for Dummies by John Paul Mueller and Luca Massaron is a comprehensive entry-level guide to Machine Learning. It helps you make sense of the programming languages and tools you need to turn machine learning-based tasks into a reality.

It explains how to get started, provides detailed discussions of how the underlying algorithms work. The book introduces a little coding in Python and R used to teach machines to find patterns and analyze results. The book is divided into six parts:

  • Part 1: Introducing How Machines Learn

  • Part 2: Preparing Your Learning Tools

  • Part 3: Getting Started with the Math Basics

  • Part 4: Learning from Smart and Big Data

  • Part 5: Applying Learning to Real Problems

  • Part 6: The Part of Tens

After reading the book, you will be able to:

  • Learn how machine learning algorithms are invaluable

  • Implement machine learning in Python and R

  • Use machine learning to accomplish practical tasks

With this book, you can take a small step into the realm of Machine Learning.


3. Best Book for Serious Learners: Machine Learning by Tom M. Mitchell

Machine Learning by Tom M. Mitchell provides a single source introduction to the primary approaches to machine learning. The book discusses several key algorithms, example data sets and project- oriented homework assignments.

The book covers the concepts and techniques from the various fields in a unified fashion. There are thirteen chapters in the book and includes the following contents:

  • Chapter 1 gives the introduction

  • Chapter 2 covers Concept Learning and the General-to-Specific Ordering

  • Chapter 3 covers Decision Tree Learning

  • Chapter 4 talks about Artificial Neural Networks

  • Chapter 5 talks about evaluating Hypotheses

  • Chapter 6 covers Bayesian Learning

  • Chapter 7 talks about Computational Learning Theory

  • Chapter 8 covers Instance-Based Learning

  • Chapter 9 covers Genetic Algorithms

  • Chapter 10 talks about Learning Sets of Rules

  • Chapter 11 covers Analytical Learning

  • Chapter 12 talks about Combining Inductive and Analytical Learning

  • Chapter 13 covers Reinforcement Learning

It is written in a clear, explanatory and precise style. The reader is not required to have prior background in artificial intelligence or statistics.


More books you may like:


4. Best Book for Hands-On Learners: Hands-On Machine Learning with Scikit-Learn, Keras and Tensor Flow

Hands-On Machine Learning with Scikit-Learn, Keras and Tensor Flow by Aurelien Geron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems.

There are nineteen chapters, and they are written and structured in such a way to make the concepts crystal clear for you.

  • Chapter 1 covers the Machine Learning Landscape

  • Chapter 2 covers End-to-End Machine Learning Project

  • Chapter 3 talks about Classification

  • Chapter 4 covers Training Models

  • Chapter 5 covers Support Vector Machines

  • Chapter 6 covers Decision Trees

  • Chapter talks about Ensemble Learning and Random Forests

  • Chapter 8 covers Dimensionality Reduction

  • Chapter 9 covers Unsupervised Learning Techniques

  • Chapter 10 gives an introduction to Artificial Neural Networks with Keras

  • Chapter 11 talks about Training Deep Neural Networks

  • Chapter 12 covers Custom Models and Training with TensorFlow

  • Chapter 13 covers Loading and Preprocessing Data with TensorFlow

  • Chapter 14 covers Deep Computer Vision Using Convolutional Neural Networks

  • Chapter 15 covers Processing Sequences Using RNNs and CNN's

  • Chapter 16 talks about Natural Language Processing with RNNs and Attention

  • Chapter 17 talks about Representation Learning and Generative Learning Using Autoencoders and GANs

  • Chapter 18 talks about Reinforcement Learning

  • Chapter 19 covers Training and Deploying TensorFlow Models at Scale

The book uses concrete examples, minimal theory, and two production-ready Python frameworks including Scikit-Learn and TensorFlow. There are exercises in each chapter for hands on learning.


5. Best Book for Completionists: Machine Learning using Python

Machine Learning using Python by U Dinesh Kumar and Manaranjan Pradhan provides a strong foundation in Machine Learning using Python libraries. The book takes a balanced approach between theoretical understanding and practical applications. 

The book is divided into 10 chapters and includes the following contents:

  • Chapter 1 gives an introduction to Machine Learning

  • Chapter 2 covers Descriptive Analytics

  • Chapter 3 talks about Probability Distributions and Hypothesis Tests

  • Chapter 4 covers Linear Regression

  • Chapter 5 talks about Classification Problems

  • Chapter 6 covers Advanced Machine Learning

  • Chapter 7 talks about clustering

  • Chapter 8 talks about forecasting

  • Chapter 9 covers Recommender Systems

  • Chapter 10 covers Text Analytics 

The book includes real-life case studies and examples to engage the reader. It provides a step-by-step approach on how to explore, build, evaluate, and optimize machine learning models.


6. Best book for visual learners: Grokking Machine Learning

Grokking Machine Learning by Luis G. Serrano teaches you how to apply ML to your projects using only standard Python code. It is written in an approachable manner with great use of very illustrative and applicable examples.

The book is divided into 13 chapters and the contents covered are:

  • Chapter 1 explains what is Machine Learning

  • Chapter 2 covers types of machine learning

  • Chapter 3 talks about linear regression

  • Chapter 4 talks about optimizing the training process: underfitting, overfitting, testing, and regularization

  • Chapter 5 talks about using lines to split our points: the perceptron algorithm

  • Chapter 6 covers a continuous approach to splitting points: logistic regression

  • Chapter 7 talks about how do you measure classification models and accuracy 

  • Chapter 8 covers the naive bayes algorithm

  • Chapter 9 talks about splitting data by asking questions: decision trees

  • Chapter 10 talks about combining building blocks to gain more power: neural networks

  • Chapter 11 talks about finding boundaries with style, support vector machines, and the kernel method

  • Chapter 12 covers ensemble learning

  • Chapter 13 covers a real-life example of data engineering and machine learning


7. Best book for Enthusiastic Learners: Practical Deep Learning

Practical Deep Learning by Ronald T. Kneusel focuses on the subfield of machine learning known as deep learning. The book explains core concepts and gives you the foundation you need to start building your own models.

After reading the book, you will be able to learn:

  • How to use classic machine learning models like k-Nearest Neighbors, Random Forests, and Support Vector Machines

  • How neural networks work and how they’re trained

  • How to use convolutional neural networks

  • How to develop a successful deep learning model from scratch

The book is divided into sixteen chapters.

  • Chapter 1 helps you to get started

  • Chapter 2 talks about using Python

  • Chapter 3 talks about using NumPy

  • Chapter 4 covers working With Data

  • Chapter 5 talks about Building Datasets

  • Chapter 6 covers Classical Machine Learning

  • Chapter 7 covers Experiments with Classical Models

  • Chapter 8 gives an introduction to Neural Networks

  • Chapter 9 talks about Training A Neural Network

  • Chapter 10 covers Experiments with Neural Networks

  • Chapter 11 talks about evaluating Models

  • Chapter 12 gives introduction to Convolutional Neural Networks

  • Chapter 13 covers experiments with Keras and MNIST

  • Chapter 14 covers Experiments with CIFAR-10

  • Chapter 15 discusses a case study on Classifying Audio Samples

  • Chapter 16 helps you in going further

This book will give you the skills and confidence to dive into your own machine learning projects. It is perfect for someone looking to break into deep learning.


More Ways to Learn Machine Learning

So these are the 6 best Machine Learning books for beginners. They serve as a great resource for those who want to learn best through reading.

There are also a few online courses on the list that let you learn Machine Learning.

We also suggest here over 70 coding resources that are free online.

It is up to you now to make the best of these resources, and take your career to the next level! Just grab the best option suitable for you and begin to learn right away.

Miranda Limonczenko

Miranda is the founder of Books on Code, with a mission to bring book-lover culture to programmers. Learn more by checking out Miranda on LinkedIn.


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