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 valuable for companies.
It can help them better understand and predict customer behavior and business trends. Machine learning is now widely used in various applications and giant companies like Google. Uber and Facebook have integrated it into their operations.
If you are new to this technology and looking for the 10 best machine learning books, here is a list.
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.
10 Best Machine Learning Books for Beginners in 2023
1. Hands-on ML with Scikit-Learn, Keras & TensorFlow by Aurelien Geron
Best For: Beginners should learn machine learning with exercises to apply what they 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 that 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
2. The Hundred-Page Machine Learning Book by Andriy Burkov
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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 essential topics 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
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
4. Machine Learning for Absolute Beginners by Oliver Theobald
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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 various 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
5. Patterns Recognition and Machine Learning by Christopher M. Bishop
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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
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
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
8. Speech and Language Processing by Daniel Jurafsky and James H. Martin
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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 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
9. Python Machine Learning by Example by Yuxi Liu
Best For: Individuals who want to learn about machine learning using Python programming.
It provides hands-on experience using machine learning through Python. It covers vast topics around machine learning algorithms, techniques, and 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
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 Python programming.
It covers various 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 new to machine learning and who 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.