10 Best Python Machine Learning Books For Beginners | 2023

Python is a programming language with many features that make it ideal for machine learning. In recent years, the popularity of machine learning has exploded, and Python has become a go-to language for many data scientists and developers who want to apply machine learning to their projects.

There are many excellent books available on machine learning with Python. This article will recommend 10 of the best Python machine learning books for beginners.

1. Python Machine Learning By Example by Yuxi (Hayden) Liu.

This book is a great starting point for beginners who want to learn about machine learning with Python. The book covers all the essential concepts and provides clear examples to help you understand how to apply them to your projects. It also has a section on deep learning, which is a hot topic in machine learning right now. It has features that make it ideal for machine learning. This book covers classification, regression, feature engineering, and model optimization.

Key features of Python Machine Learning By Example book

• It is a great starting point for those who want to learn machine learning with Python
• The book covers essential concepts and provides clear examples
• It has a section on deep learning
• It covers topics such as classification, regression, feature engineering, and model optimization
• The book is well-written and easy to understand.

If you are a beginner who wants to learn machine learning with Python, this book is for you. It covers all the essential concepts and provides clear examples to help you understand how to apply them to your projects.

2. Grokking Machine Learning by Andrew Trask.

If you want to understand how machine learning works, this book is for you. The author does a great job of explaining the concepts in plain English and providing examples that help illustrate his points. It covers comprehensive topics such as linear regression, Support Vector Machines, neural networks, and deep learning.

Key features:

• Teaches you the basics of machine learning in an easy to understand the manner
• Covers a wide range of topics
• Provides clear examples to help illustrate key concepts.
• Written by a well-respected authority on the subject.

It- Perfect for absolute beginners.

If you want to learn machine learning and understand how it works, this book is for you.

3. Introduction to Machine Learning with Python by Andreas Mueller and Sarah Guido.

A practical approach to the subject, this book is designed to teach readers how to use machine learning algorithms to complete real-world tasks. Covering a wide range of topics, including supervised and unsupervised learning, deep learning, and dimensionality reduction, the book provides an intuitive understanding of the principles behind each technique. Mueller and Guido also show readers how to implement these algorithms using the popular Python libraries scikit-learn and TensorFlow.

Key features:

• Offers a gentle introduction to the subject for those with little or no background in machine learning
• Introduces each technique with clear explanations and plenty of code examples
• Shows how to use popular Python libraries, such as scikit-learn and TensorFlow, to implement machine learning algorithms
• Includes coverage of deep learning, a cutting-edge field of machine learning
• Provides detailed instructions for completing several real-world tasks

Andreas Mueller and Sarah Guido’s “Introduction to Machine Learning with Python” is excellent for beginners looking to get started with machine learning. The book provides clear explanations of each technique and code examples showing how to use the popular Python libraries scikit-learn and TensorFlow to implement each algorithm.

4. Machine Learning (in Python and R) by Jason Brownlee.

A comprehensive guide to the subject, this book begins with an introduction to machine learning and the types of problems it can be used to solve. Brownlee then walks readers through a series of tutorials that show how to implement machine learning algorithms in Python and R. The book also covers various topics such as feature selection, model evaluation, and working with streaming data.

Key features:

• Provides a broad introduction to machine learning
• Walks readers through a series of tutorials using both Python and R
• Covers a variety of topics such as feature selection, model evaluation, and working with streaming data
• Includes end-of-chapter exercises to help readers test their understanding
• Is accompanied by a companion website that provides code and datasets
• Is suitable for both beginners and experienced practitioners

This book is an excellent choice for those who want to learn machine learning from scratch. It provides a broad introduction to the subject and walks readers through a series of tutorials using Python and R. The book covers various topics, making it ideal for both beginners and experienced practitioners.

5. Machine Learning For Absolute Beginners: A Plain English Introduction by Oliver Theobald.

This book is a beginner-friendly guide to machine learning. This book starts with a gentle introduction to the concepts and then moves to more advanced topics. Theobald covers various issues, including supervised and unsupervised learning, deep learning, and natural language processing. He also provides clear explanations of implementing machine learning algorithms using the popular Python library sci-kit-learn.

Key features

• A gentle yet comprehensive introduction to machine learning
• clear explanations of the most popular machine learning algorithms
• Coverage of both supervised and unsupervised learning
• An overview of deep learning
• Explanations of how to implement machine learning algorithms using Python’s sci-kit-learn library

If you’re looking for a beginner-friendly introduction to machine learning, this book is for you. It also serves as a great reference guide for more experienced practitioners.

6. Machine Learning with Python Cookbook by Prateek Joshi.

A practical guide to the subject, this book provides readers with recipes that show how to use the popular Python libraries scikit-learn and TensorFlow to build machine learning models. The book also covers various topics such as regression, classification, clustering, and dimensionality reduction.

Key features

• Over 100 recipes to help you overcome various machine learning challenges
• Techniques to improve and tune your machine learning models
• Cover a wide range of topics
• Use popular Python libraries such as scikit-learn and TensorFlow
• Tips and tricks to improve your machine learning model’s performance

This book is a great place to start for anyone who wants to learn more about machine learning and how to use Python to build models.

7. Machine Learning with Python for Everyone by Mark Fenner

This book is a comprehensive guide that starts with the basics of machine learning and Python. It then moves to more advanced topics such as deep learning, natural language processing, and big data. Fenner also provides clear explanations of implementing machine learning algorithms using the popular Python libraries sci-kit-learn and TensorFlow.

Key features

• Covers the basics of machine learning and Python
• Provides clear explanations of how to implement machine learning algorithms
• Uses popular Python libraries such as sci-kit-learn and TensorFlow

This book is a good choice if you want a comprehensive guide to machine learning that covers both the basics and more advanced topics. Fenner does a great job of explaining complex concepts that are easy to understand, and the code examples are beneficial.

8. Python Machine Learning by Sebastian Raschka and Vahid Mirjalili.

A bestselling guide to the subject, this book provides a comprehensive introduction to machine learning and its applications. Raschka and Mirjalili show readers how to implement machine learning algorithms using the popular Python library sci-kit-learn. They also cover a wide range of topics, such as supervised and unsupervised learning, deep learning, and big data.

Key features

• Offers clear explanations of the most important machine learning concepts
• Features implementation examples using the sci-kit-learn library
• Provides a broad range of topics, including supervised and unsupervised learning, deep learning, and big data
• Written by Sebastian Raschka, a leading figure in the global Python community

This book is a comprehensive guide to machine learning that covers both the subject’s theoretical foundations and practical applications. It is written by Sebastian Raschka, a well-known figure in the global Python community. The book features clear explanations of the most important machine learning concepts and includes implementation examples using the popular sci-kit-learn library.

9. Machine Learning: 4 Books in 1 by Frank Kane.

If you’re looking for a comprehensive introduction to machine learning, this book is for you. It includes four books in one: “Supervised Learning Algorithms,” “Unsupervised Learning Algorithms,” “Reinforcement Learning Algorithms,” and “Deep Learning Algorithms.” The first two sections focus on the basics of machine learning, while the latter two covers more advanced topics.

Key features

• Comprehensive coverage of machine learning algorithms
• Code examples in Python
• Helpful tips and tricks for working with machine learning algorithms
• Explanations of how each algorithm works
• Insight into the pros and cons of each algorithm

This book covers a wide range of machine learning topics, making it a great resource for beginners. The code examples are also helpful for readers who want to try out the algorithms themselves.

10. Ultimate Step by Step Guide to Machine Learning Using Python by Oliver Theobald.

This book is an excellent resource if you’re just getting started with machine learning and Python. It covers all the basics of machine learning clearly and concisely, and it provides practical examples to help you understand how each concept works. The book also includes a section on deep understanding, which is a hot topic in machine learning.

Key features

• Concise and easy to understand
• Covers all the basics of machine learning
• Provides practical examples
• Includes a section on deep learning

This is an excellent book for beginners who want to learn more about machine learning and Python. It also includes a section on deep understanding, which is a hot topic in machine learning right now.

These books are great options for anyone who wants to learn more about machine learning with Python. No matter your level of experience, you will find a book on this list that is right for you.

FAQs

What is the best machine learning book for beginners?

There are several excellent machine learning books for beginners. Some of our favorites include “Python Machine Learning By Example” by Yuxi (Hayden) Liu and “Grokking Machine Learning” by Andrew Trask. These books are great for beginners because they provide clear and concise explanations of how to implement machine learning algorithms using the popular Python library sci-kit-learn.

Can I learn machine learning without Python?

Yes, you can learn machine learning without Python. There are many popular languages used for machine learning, including R, Java, and MATLAB. However, Python is one of the most popular languages for machine learning because it has great libraries and tools that make development easier.

Which framework is best for Python machine learning?

There are many different frameworks that you can use for Python machine learning. Some of the most popular include TensorFlow, Keras, and sci-kit-learn. These framework libraries provide many different tools and features that make development easier.