Reading machine learning books can help to understand what machine learning is about.
Machine Learning allows us to automate tasks and make continuous improvements to daily tasks and organizational strategies.
According to Dave Waters,
“Machine Learning will automate jobs that most people thought could only be done by people.”
What is Machine Learning?
So, what exactly is machine learning? Machine learning applies artificial intelligence to automate programs. The first part of machine learning involves collecting data samples to make future predictions.
The aim of machine learning is to let the computer analyse your patterns and learn automatically. Also, with machine learning, you can analyse large sets of data and deliver quick and accurate results. Machine learning has many categories, and this article on Different Types of Machine Learning will clarify its types.
For a quick overview of machine learning, watch this Machine Learning Basics video:
How Machine Learning Books Can Help Beginners
Books can help you learn machine learning and its concept better, and as a novice user, reading machine learning books will help bring you up to speed with respect to machine learning industry trends. Here are some reasons books can help you learn machine learning better.
1. Great Investment
Books generally cost lower and can give you all the knowledge you want. With the right book, you can get valuable knowledge that will help you for years to come.
2. Quick Reference
Stuck on a concept, pull out a book on that concept and you will find all the information you need to know. Also, you can use a book as a reference anytime you want, regardless of how far ahead you are in your career. Say you forget a concept of statistics and want to brush up on your skills, a book that you used to study the concept initially will help refresh your memory.
3. In-depth Understanding
Books help you grasp a concept better since they are so detailed. You can also mark concepts you would like to come back to later. Many people learn via reading, and books are a great way to help them learn machine learning.
Apart from reading books, having a brief idea of the concepts of machine learning can also help you understand the same better.
21 Best Machine Learning Books
Here are some of the best machine learning books.
1. The Hundred Page Machine Learning Book – Andriy Burkov
This book is easy to understand and covers all the basic machine learning concepts. This 100 -page book has become quite popular among many machine learning experts and has been endorsed by leaders such as Peter Norvig.
The book covers all the perspectives of machine learning succinctly and contains various machine learning topics.
2. Machine Learning for Absolute Beginners: A Plain English Introduction – Oliver Theobald
The second edition of Machine Learning for Absolute Beginners is exactly as the name states: a book that has all the machine learning concepts for beginners. It has all explanations in simple English, and readers don’t need to have any coding experience to make sense of the topics covered.
3. Introduction to Machine learning with Python: A Guide for Data Scientists – Anderas Muller and Sarah Guido
Even if you are a beginner in the field of machine learning with knowledge of Python, this machine learning book can help you build practical solutions of your own. Reading it, you can learn the steps to create an application of your own with Python and the scikit-learn library.
4. Deep Learning with Python – Francois Chollet
If you have prior knowledge of Python, then this book can help you understand the concepts if deep learning with Python using the Keras library. It contains detailed explanations and practical examples. Also, you will be able to practice with applications in computer vision and generative models, giving you hands-on skills to use deep learning in your own projects.
5. An Introduction to Statistical Learning with Applications in R – Graeth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
This is an introductory book with the most basic applications and uses of machine learning in predictive data analysis.
It’s a combined work by many authors who have experience teaching machine learning and working with predictive analysis.
The book can also be used by undergraduates in computer science, engineering, and statistics.
6. Understanding Machine Learning: From Theory to Algorithms – Shai Shalev-Shwartz and Shai Ben-David
The book was initially published by Cambridge University to help students learn the basics of machine learning. It also helps readers familiarize with the key algorithms in the field.
The book transforms mathematical derivations and machine learning principles into practical algorithms. The book also covers various topics that have been addressed by previous textbooks.
7. Deep Learning (Adaptive Computation and Machine Learning) – Ian Goodfellow, Yoshua Bengio, and Aron Courville
The book introduces a broad range of topics in Deep Learning and even covers mathematical and conceptual formulas.
These include numerical computation, linear algebra, probability, information theory, etc.
Since the book is written by experts in the industry, it also covers deep learning best practices adopted by machine learning professionals.
8. Pattern Classification – Richarr O. Duda, Peter E. Hart, and David G. Stork
The book was first published in 1973 when the concept of machine learning was just a distant theory. This book is now a classic reference for all things of machine learning.
The second edition of the book is also worth reading and contains updated information on the trends of machine learning.
9. Paradigms of Artificial Intelligence Programming – Peter Norvig
Authored by a thought leader in machine learning, the paradigms of machine learning is considered one of the best machine learning books for beginners ever written.
Thought the text and concepts are easy to follow, you still need some programming knowledge to read this. If you have programming knowledge, then make sure you read this book for a thorough understanding of AI and machine learning.
10. Machine Learning: The New AI – Ethem Alpaydin
This book has been published recently and covers details about the algorithms used on data sets which can help programmers create codes from these data sets. Alpaydin has also authored Introduction to Machine Learning and is considered an expert in the field of machine learning.
11. Artificial Intelligence: A Modern Approach – Stuart Russell and Peter Norvig
This book covers a wide array of topics in machine learning and artificial intelligence. This book is meant for novices in machine learning, and anyone with prior experience in the field will find it simplistic. This book covers techniques for solving algorithms and scaling machine learning projects.
12. Machine Learning: The Art and Science of Algorithms that Make Sense of Data – Peter Flach
This book covers the real-world application of machine learning. It gives a refresher on the basics of machine learning and is more helpful for intermediate level machine learning professionals.
The book covers all the machine learning in detail, more so than any other book of this kind.
13. Python Machine Learning: Sebestain Raschka
This is a language-specific book that focuses on using machine learning with Python programming language. It is also believed to be one of the best machine learning books since most machine learning professionals start off with Python. If you are a Python developer, then this is one of the top machine learning books for beginners. The book also covers how to use the scikit-learn library and how to apply data analysis.
14. Data Science from Scratch: First Principles with Python – Joel Grus
This is another in the language-based machine learning books list, but it’s more detailed and shorter than the previous book we discussed.
This book starts with the concepts of Python before moving onto machine learning, so even if you are a beginner in Python, you should be able to follow the concepts in this book.
The writing is very clear and to the point, which is what gets you the knowledge you are looking for. This book, though designed for beginners, is recommended for intermediate level developers who don’t need to learn about data analysis.
15. Make Your Own Neural Network – Tariq Rashid
Though this book is about neural networks and how to use them in machine learning, it also some concepts about Python.
This is a great book if you want to learn all the details about the building and deploying neural networks. It includes examples, and the writing is easy to follow, making it a great choice for diving into the concept of machine learning with neural networks.
16. Machine Learning: The Ultimate Beginners Guide For Neural Networks, Algorithms, Random Forests and Decision Trees Made Simple – Alexa Miller
Machine learning is not just limited to one concept but has a wide variety of uses, and these are expanding even more. With this book, you can get a thorough understanding of everything that forms machine learning, such as forming decision trees and algorithms. If you are a beginner and not as familiar with everything machine learning can do, this book is a great way to familiarize yourself with the use of machine learning and speech recognition. This book can be your stepping stone into the world of machine learning.
17. Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series) – Kevin P. Murphy
This book is written in an informal style and contains pseudo-code for many important algorithms. All the concepts have illustrative representations as well, so if you are a visual learner, this is the perfect book for you. The worked examples are drawn from applications like biology, computer processing, and robotics.
It adopts a structured, model-based approach with graphic elements to explain concepts simply and clearly.
18. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) – Trevor Hastie, Robert Tibshirani, Jerome Friedman
This is among the top machine learning books, which describes the conceptual framework of a machine learning model in various fields like marketing, medicine, and finance. It is written with a statistical framework but with a focus on explaining concepts rather than on statistics.
It also has a lot of graphics and concepts are explained with practical examples. This updated edition covers many models and ensemble methods that are not included in the first edition.
19. Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies – John D. Kelleher Brian Mac Namee, Aoife D’Arcy
This is basically a textbook that focuses on machine learning concepts for data analysis. It contains theory as well as practical applications of data analysis in machine learning.
Authored by professionals who have extensive experience in teaching machine learning, the book is suitable for undergraduates in engineering, statistics, and computer science, as well as professionals with a basic technical background. It is considered one of the top machine learning books for beginners.
20. Machine Learning: A Bayesian and Optimization Perspective (Net Developers) – Sergios Theodoridis
This book talks about all the major machine learning in a variety of disciplines like statistics, adaptive signal processing and computer science. It focuses on the reasoning behind developing the statistics and explains all the statistics in detail with examples.
The book starts with the basic machine learning concepts and gradually moves on to the recent trends in machine learning, ranking it is among the top machine learning books for beginner
21. Machine Learning with TensorFlow – Nishant Shukla
Not only does the book have various machine concepts, but it also contains practical user examples of coding with TensorFlow in Python. It will help you learn the basics with classic predictions and cluster algorithms.
It also explores deep learning concepts like neural networks, autoencoders, and reinforcement learning. One of the top machine learning books for beginners, this is your all-inclusive guide to machine learning and TensorFlow.
Conclusion
The basic difference in machine learning and traditional programming is that in traditional programming, data is inserted into a program and we get an output, whereas in machine learning the data we feed into the system runs on the machine and the machine creates its own output.
With machine learning, your computer will start analysing patterns and tasks to automate processing.
If you also want to become a Machine Learning Engineer, enroll in the Machine Learning Course and elevate your career.
That is a great list you have compiled there.