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One of the ways to learn or strengthen your skills in any field is to read a good book about the subject matter.
Of course, only some books would be beneficial to your learning endeavors.
It all depends on the presentation, the language in the book, and other factors.
Learning Python programming can be intimidating.
So, choose resources that make your learning journey easier.
Here are the 9 best Python books for machine learning and what to avoid in your learning curve.
- The Importance of Python in Machine Learning
- What is the best book for machine learning in Python for beginners?
- 1. Python Crash Course, 2nd Edition
- 2. Head First Python: A Brain-Friendly Guide, 2nd Edition
- 3. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition
- 4. Automate the Boring Stuff with Python, 2nd Edition: Practical Programming for Total Beginners
- 5. Machine Learning For Dummies 2nd Edition
- 6. Python Machine Learning for Beginners
- 7. Machine Learning for Absolute Beginners: A Plain English Introduction
- 8. Introduction to Machine Learning with Python: A Guide for Data Scientists
- 9. Python Machine Learning: Machine Learning and Deep Learning with Python, Scikit-learn, and TensorFlow
- What to Avoid in your Learning Curve
- Conclusion
The Importance of Python in Machine Learning
Python is a versatile tool that’s dominant in diverse contexts. For example, Engineers and Data Scientists use it to accomplish a set task.
It’s also vital in Machine Learning.
Python automates operations. It’s also great for application development and diverse aspects of Data Science like Artificial Intelligence, Data Analysis, and Machine Learning.
What is the best book for machine learning in Python for beginners?
Books are one of your best bets for learning Python programming for machine learning.
With the right set of books, you can learn everything there is to know about Python programming.
They’ll get you to the doorstep of the Python language for machine learning.
But there are too many books on the subject matter, increasing the chances of being overwhelmed.
Don’t worry; We did all the hard work for you.
Here are the 9 Best Python Books for Machine Learning and what to avoid while learning.
Ready?
Let’s dig in.
1. Python Crash Course, 2nd Edition
Figure 1: The cover page of Python Crash Course. (AP/Amazon)
| Author | Eric Matthews |
| Publication Date | May 3, 2019 |
| Dimensions | 7 x 1.25 x 9.19 inches |
| Print Length | 544 pages |
This book gives you a head start in Python as it takes you through the basics. Eric explains every concept concisely to aid comprehension.
You’ll learn the basics of creating video games, data visualizations, and web applications.
It also introduces beginners to resource libraries like GitHub and Django. If you can wait until January 2023, you’ll get the book’s third edition.
2. Head First Python: A Brain-Friendly Guide, 2nd Edition
Figure 2: The cover page of Head first Python: A Brain-Friendly Guide. (AP/Amazon)
| Author | Paul Barry |
| Publication Date | December 27, 2016 |
| Dimensions | 8 x 1.3 x 9.2 inches |
| Print Length | 622 pages |
Paul decorated Head First Python: A Brain-Friendly Guide with illustrations to make it easily digestible.
Experts in the field also commend this book for its clarity and impeccable use of humor that keep readers hooked till the end.
The book teaches the fundamentals of Python, syntax, and data wrangling and offers a practical guide to creating applications.
You should know how to develop apps for Android phones and other engines after studying it.
3. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition
Figure 3: The cover page of Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow. (AP/Amazon)
| Author | Aurélien Géron |
| Publication Date | October 4, 2022 |
| File Size | 26772 KB |
| Print Length | 1457 pages |
This book will take you from being a novice Python programmer to a machine learning expert. Although the length is intimidating, it documents practical knowledge.
Géron explored all the tools, concepts, and techniques every engineer in machine learning needs to advance in the field.
Don’t worry; the journey is progressive, so you won’t feel overwhelmed. It starts with an introduction to Machine-learning and then explores different techniques- from simple to deep learning.
4. Automate the Boring Stuff with Python, 2nd Edition: Practical Programming for Total Beginners
Figure 4: The cover page of Automate the Boring Stuff with Python, 2nd Edition: Practical Programming for Total Beginners. (AP/Amazon)
| Author | Al Sweigart |
| Publication Date | November 12, 2019 |
| File Size | 25787 KB |
| Print Length | 591 pages |
Whether you’re a beginner or trying to improve your skills, Al Sweigart wrote this book for you. You’ll learn to write programs for task automation, Python 3 basics, Gmail and Google sheets automation, input validation, etc.
5. Machine Learning For Dummies 2nd Edition
Figure 5: The Cover page of Machine Learning for Dummies. (AP/Amazon)
| Author | John Paul Mueller, Luca Massaron |
| Publication Date | January 12, 2021 |
| File Size | 7489 KB |
| Print Length | 443 pages |
What’s a learning experience without one of the “for dummies” book series?
This “for dummies” book helps you kickstart your journey in machine learning.
It covers topics about AI and machine learning history, teaches practical knowledge with the latest datasets, helps you create and test models, and shows you how to deploy machine learning in problem-solving.
6. Python Machine Learning for Beginners
Figure 6: The Cover page of Python Machine Learning for Beginners. (AP/Amazon)
| Author | AI Publishing |
| Publication Date | October 23, 2020 |
| Dimensions | 6 x 0.69 x 9 inches |
| Print Length | 304 pages |
AI Publishing designed this book to be as practical as possible. The first part of the book explores the theoretical aspects of machine learning.
The second part dives into practical machine-learning techniques.
Generally, it’ll expose you to data visualization with Seaborn and Pandas libraries, data clustering with Sklearn Library, deep learning with Python TensorFlow 2.0, etc.
7. Machine Learning for Absolute Beginners: A Plain English Introduction
Figure 7: The Cover page of Machine Learning for Absolute Beginners: A Plain English Introduction. (AP/Amazon)
| Author | Oliver Theobald |
| Publication Date | December 31, 2020 |
| File Size | 19903 KB |
| Print Length | 181 pages |
Designed to get you started in machine learning with Python, this book teaches you the essential algorithms and principles in the field.
It’s written with clear-cut clarity and uses visually illustrated examples.
You’ll learn neural network basics, data preparation for analysis, machine-learning model creation, etc.
8. Introduction to Machine Learning with Python: A Guide for Data Scientists
Figure 8: The Cover page of Introduction to Machine Learning with Python: A Guide for Data Scientists. (AP/Amazon)
| Author | Andreas Mueller, Sarah Guido |
| Publication Date | September 26, 2016 |
| File Size | 42278 KB |
| Print Length | 402 pages |
This book shows you how to create machine-learning solutions with Python, regardless of your proficiency.
You’ll also learn to build your machine-learning applications with the Scikit-Learn library and the practical aspects of machine-learning algorithms.
9. Python Machine Learning: Machine Learning and Deep Learning with Python, Scikit-learn, and TensorFlow
Figure 9: The Cover of Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow. (AP/Amazon)
| Author | Sebastian Raschka, Vahid Mirjalili |
| Publication Date | December 12, 2019 |
| File Size | 24250 KB |
| Print Length | 774 pages |
Python Machine Learning is an extensive guide to deep learning and machine learning in Python.
It takes you step-by-step through the basics of machine-learning techniques and algorithms.
What to Avoid in your Learning Curve
● Don’t overwhelm yourself. With many books, it’s easy to get overwhelmed. So, take the books one after the other. Frankly speaking, you don’t have to read every single book on this list.
● Don’t push yourself too hard. Refrain from pushing yourself too hard. These books are structured progressively to go at your pace. Only move on to the next principle after you’ve mastered it.
● Don’t skip the exercises. Take your time to go through the exercise, as they’re vital to your growth.
Conclusion
These Books for Machine Learning would get you started on your journey. But you should only invest in books that resonate with your learning style.