10 Best Machine Learning Books for Beginners in 2024
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.
8. Best Book for ML Engineers: Python Machine Learning By Example
Python Machine Learning By Example by Yuxi (Hayden) Liu serves as a comprehensive gateway into the world of machine learning (ML). The book provides actionable insights on the key fundamentals of ML with Python programming.
The book demonstrates implementations of algorithms in Python, both from scratch and with libraries. It includes best practice tips and real-world examples using PyTorch, TensorFlow, scikit-learn, and pandas. Here;s what you’ll get from the book:
Follow machine learning best practices throughout data preparation and model development
Build and improve image classifiers using convolutional neural networks (CNNs) and transfer learning
Develop and fine-tune neural networks using TensorFlow and PyTorch
Analyze sequence data and make predictions using recurrent neural networks (RNNs), transformers, and CLIP
Build classifiers using support vector machines (SVMs) and boost performance with PCA
Avoid overfitting using regularization, feature selection, and more
The topics covered in the book are:
Chapter 1 talks about Getting Started with Machine Learning and Python
Chapter 2 talks about Building a Movie Recommendation Engine
Chapter 3 talks about Predicting Online Ad Click-Through with Tree-Based Algorithms
Chapter 4 talks about Predicting Online Ad Click-Through with Logistic Regression
Chapter 5 talks about Predicting Stock Prices with Regression Algorithms
Chapter 6 talks about Predicting Stock Prices with Artificial Neural Networks
Chapter 7 talks about Mining the 20 Newsgroups Dataset with Text Analysis Techniques
Chapter 8 talks about Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling
Chapter 9 talks about Recognizing Faces with Support Vector Machine
Chapter 10 talks about Machine Learning Best Practices
Chapter 11 talks about Categorizing Images of Clothing with Convolutional Neural Networks
Chapter 12 talks about Making Predictions with Sequences Using Recurrent Neural Networks
Chapter 13 talks about Advancing Language Understanding and Generation with Transformer Models
Chapter 14 talks about Building An Image Search Engine Using Multimodal Models
Chapter 15 talks about Making Decisions in Complex Environments with Reinforcement Learning
If you're a machine learning enthusiast, data analyst, or data engineer highly passionate about machine learning and want to begin working on machine learning assignments, this book is for you.
9. Best Book for Newbies: Fundamentals of Machine Learning for Predictive Data Analytics
Fundamentals of Machine Learning for Predictive Data Analytics by John D. Kelleher, Brian Mac Namee, and Aoife D'Arcy is a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Technical and mathematical material is augmented with explanatory worked examples. The book includes case studies that illustrate the application of these models in the broader business context.
The book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples.
This book strikes a great balance between theory and practicality. It is easy to follow and provides thorough walk-throughs of sample problems. This book will serve you well on your machine learning journey.
10. Best ML Introductory Book: The StatQuest Illustrated Guide To Machine Learning
The StatQuest Illustrated Guide To Machine Learning by Josh Starmer builds the ML foundational concepts and exposes complex ideas in a very well-designed and planned way. The book takes the machine learning algorithms, no matter how complicated, and breaks them down into small, bite-sized pieces that are easy to understand.
The book is well-organized and covers a wide range of topics in machine learning. Each concept is clearly illustrated to provide you with an intuition about how the methods work. The book covers:
Fundamental Concepts in Machine Learning
Cross Validation
Fundamental Concepts in Statistics
Linear Regression
Gradient Descent
Logistic Regression
Naive Bayes
Assessing Model Performance
Preventing Overfitting with Regularization
Decision Trees
Support Vector Classifiers and Machines (SVMs)
Neural Networks
The book starts with the basics, showing you what machine learning is and what its goals are, and builds on those, one picture at a time. The illustrations and humor make it way more digestible.
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.
Coursera:
Machine Learning by Andrew Ng is a 60-hours course that provides a broad introduction to Machine Learning.
Machine Learning with Python is a 22-hours course that dives into the basics of Machine Learning using Python.
Codecademy: Learn the Basics of Machine Learning is a 20-hours course covers the foundational machine learning algorithms that will help you advance in your career. For more on Codecademy Pro, see my Codecademy Pro review.
Udemy: Machine Learning A-Z: Hands-On Python & R In Data Science is a high rated 45-bours course. It will help you to create Machine Learning Algorithms in Python and R from two Data Science experts.
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.