Top 10 Best Books for Machine Learning

Kshitiz Sharan
6 min readSep 9, 2022


Machine learning is a fast-growing field of research, and there’s no shortage of books to help you get started. If you’re new to the subject, these books will give you an introduction into machine learning and help prepare you for what’s next.

Hands-on Machine Learning with Scikit-Learn and TensorFlow

What is it? This book is a combination of two previously published books, “Practical Machine Learning with Python” (the first edition) and “Hands-On Data Science with Scikit-Learn and TensorFlow”. It covers the basics of machine learning, but also goes into more advanced topics like neural networks.

Hands-on Machine Learning with Scikit-Learn and TensorFlow is a book that teaches you how to apply machine learning techniques in practical ways. It’s aimed at beginners and intermediate level learners, so if you’re just getting started with ML, this book will help you understand the concepts behind it. It also covers how to use these tools effectively as well as provides lots of example code for each method explained in detail.

Machine Learning: A Probabilistic Perspective

This book is a great introduction to machine learning, and it’s also a good reference for understanding how the algorithms work. It’s not as heavy on theory as some other books, but it does have some meaty chapters on applications of ML in real-world contexts.

The author uses simple examples that you can easily understand without being an expert in math or computer science. He also explains how each algorithm works, so you can appreciate its strengths and weaknesses immediately after reading through his explanations of each one (there are two chapters devoted specifically to this topic). The book includes detailed explanations about data sets used in machine learning models; these are essential if you’re going to get any sense of what makes them work well or poorly!

The Elements of Statistical Learning: Data Mining, Inference, and Prediction

The Elements of Statistical Learning: Data Mining, Inference and Prediction is a book that provides an overview of machine learning using the R programming language. It is available online for free and can be downloaded here.

This book is great for beginners because it teaches you how to use R and its libraries to do basic machine learning tasks such as clustering or regression analysis. The book also has some examples on how to use R in real life scenarios such as analyzing text corpora (eBooks) which helps you get a feel for how your data will be used by others who may want access too it in different ways than just reading through books like this one!

Neural Networks and Deep Learning

Neural networks are a type of machine learning model, which means they use the principles of neuroscience to learn. They’re often used for image recognition, speech recognition and even self-driving cars. Deep learning is one specific type of neural network that has become very popular in recent years because it’s able to perform tasks better than traditional neural networks while consuming less data.

Deep learning uses multiple layers to represent information as data points (each layer representing more abstracted pieces). Each layer learns independently from its predecessors — for example: if you have one layer that recognizes an object and another layer that recognizes different types of objects then each will be trained separately so both can process their own information on top of what each other has learned about this particular category (e.g., “dogs,” “cats”). This way you end up with two separate models but only needing one model instead having two separate ones! It also allows us greater flexibility since we don’t need perfect labels at all times since there won’t always be 100% accuracy guaranteed by our system — we’ll simply make adjustments based on feedback received during testing rounds until something meets our standards efficiently enough without too much hassle along the way.”

Pattern Recognition and Machine Learning

Pattern recognition and machine learning are related. Pattern recognition is a subset of machine learning, which is also a branch of artificial intelligence (AI). Pattern recognition is a branch of computer science and statistics that deals with learning from examples. It has applications in many areas such as computer vision and pattern analysis for image processing, speech recognition, handwriting recognition, automatic speech transcription or translation, biometrics (e.g., face detection) etc..

Pattern recognition can be applied to solve practical problems by identifying patterns in data sets or images; for example:

● finding similar objects from different views or angles;

● recognizing handwritten numbers from random samples taken from different angles;

● detecting anomalies based on statistical properties such as mean deviation or median absolute deviation;

An Introduction to Statistical Learning

This book is written by the creator of this list, and it’s a great introduction to statistical learning for beginners. It’s not just about machine learning; it also covers basic statistics and data science, which means that if you’re already familiar with those topics but not enough to start your own project from scratch, this book will give you some insight into how they work together.

The author does a good job at explaining concepts in layman’s terms without getting too technical or boring (which is often the case when reading technical texts). He doesn’t just talk about what makes a good algorithm — he tells stories about why these algorithms are useful for real-world problems. In addition to explaining how ML works under the hoods of various algorithms (like logistic regression), he explains how humans can use those methods in their own lives better than computers alone could ever do!

Introduction to Machine Learning with Python

Machine learning is the process of using computer hardware and software to learn from data. It’s a big field, with plenty of advanced concepts that can be difficult to grasp on your first go-round.

This book introduces you to basic machine learning concepts by explaining how they work at an intuitive level. You’ll learn about different types of models and evaluate them against each other, as well as how to improve your model through various techniques like regularization or parameter tuning. You’ll also see what it takes for real-world applications — and why these aren’t just for academics!

Machine Learning: Algorithms and Applications

Machine Learning: Algorithms and Applications is a good place to start for beginners. The book covers a lot of material, including how to apply machine learning to real-world problems, but it’s also very thorough — and thus not always the best choice for advanced topics. The book is available as a free PDF download or you can buy the hard copy. Machine Learning: A Probabilistic Perspective This classic by Kevin Murphy, Andrej Karpathy, and Joshua Tenenbaum covers a lot of ground: how to represent data, how to build models from it, how to make predictions with those models.

The Hundred-Page Machine Learning Book

The Hundred-Page Machine Learning Book is a good book for beginners and people who want to learn machine learning in a short time.

It’s also good if you want to self-study. If you don’t have an instructor or professor, this book will give you the knowledge needed to get started with machine learning on your own.

Master Data Science with R — Advanced Level Requirements, Techniques and Projects

Master Data Science with R — Advanced Level Requirements, Techniques and Projects

This book is for advanced level data science. It teaches you how to use the R programming language to perform Machine Learning (ML) tasks on large data sets. The book also includes a series of projects that you can perform using these techniques.

Good resources for learning machine learning.

● Books: The Art of Data Science: A Guide to Becoming a More Productive and Effective Data Scientist

The Art of Data Science is an excellent book for learning about data science. It covers all aspects of the field, including machine learning, statistical analysis, visualization and programming.

● Machine Learning for Quantitative Finance by Christopher H. Manning, Richard S. Teal, and Robert J. Wilson (Wiley Finance)

● The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman (Princeton University Press)

● The Elements of Statistical Machine Learning with Python by Wesley Rohn et al (Packt Publishing)


We hope you’ve found this list helpful for discovering new books on machine learning. If you have any questions about or recommendations for these or other books, please reach out in the comments below!