Another 10 Free Must-Read Books for Machine Learning and Data Science
Here's a third set of 10 free books for machine learning and data science. Have a look to see if something catches your eye, and don't forget to check the previous installments for reading material while you're here.
Who likes free books?
Considering the popularity of the previous installments of the "Free Must-Read Books" series, a third edition of quality (free!) books seemed like a good idea. In here you will find a few books on elementary machine learning, a few on general machine topics of interest such as feature engineering and model interpretability, an intro to deep learning, a book on Python programming, a pair of data visualizations entrants, and twin reinforcement learning efforts.
There's nothing left to say but "get reading!"
1. Mathematics for Machine Learning
By Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong
We are in the process of writing a book on Mathematics for Machine Learning that motivates people to learn mathematical concepts. The book is not intended to cover advanced machine learning techniques because there are already plenty of books doing this. Instead, we aim to provide the necessary mathematical skills to read those other books.
2. The Hundred-Page Machine Learning Book
By Andriy Burkov
All you need to know about Machine Learning in a hundred pages. Supervised and unsupervised learning, support vector machines, neural networks, ensemble methods, gradient descent, cluster analysis and dimensionality reduction, autoencoders and transfer learning, feature engineering and hyperparameter tuning! Math, intuition, illustrations, all in just a hundred pages!
The read first, buy later principle implies that you can freely download the book, read it and share it with your friends and colleagues. If you liked the book, only then you have to buy it.
3. Dive into Deep Learning
By Aston Zhang, Zack C. Lipton, Mu Li, and Alex J. Smola
We set out to create a resource that could (1) be freely available for everyone, (2) offer sufficient technical depth to provide a starting point on the path to actually becoming an applied machine learning scientist, (3) include runnable code, showing readers how to solve problems in practice, and (4) that allowed for rapid updates, both by us, and also by the community at large, and (5) be complemented by a forum for interactive discussion of technical details and to answer questions.
4. Feature Engineering and Selection: A Practical Approach for Predictive Models
by Max Kuhn and Kjell Johnson
The goals of Feature Engineering and Selection are to provide tools for re-representing predictors, to place these tools in the context of a good predictive modeling framework, and to convey our experience of utilizing these tools in practice. In the end, we hope that these tools and our experience will help you generate better models.
Like in Applied Predictive Modeling, we have used R as the computational engine for this text.
5. Interpretable Machine Learning
By Christoph Molnar
All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
The book focuses on machine learning models for tabular data (also called relational or structured data) and less on computer vision and natural language processing tasks. Reading the book is recommended for machine learning practitioners, data scientists, statisticians, and anyone else interested in making machine learning models interpretable.
6. A Byte of Python
By Swaroop C. H.
"A Byte of Python" is a free book on programming using the Python language. It serves as a tutorial or guide to the Python language for a beginner audience. If all you know about computers is how to save text files, then this is the book for you.
At the BBC data team, we have developed an R package and an R cookbook to make the process of creating publication-ready graphics in our in-house style using R’s ggplot2 library a more reproducible process, as well as making it easier for people new to R to create graphics.
The cookbook below should hopefully help anyone who wants to make graphics[.]
8. Data Visualization: A practical introduction
By Kieran Healy
You should look at your data. Graphs and charts let you explore and learn about the structure of the information you collect. Good data visualizations also make it easier to communicate your ideas and findings to other people. Beyond that, producing effective plots from your own data is the best way to develop a good eye for reading and understanding graphs—good and bad—made by others, whether presented in research articles, business slide decks, public policy advocacy, or media reports. This book teaches you how to do it.
9. Algorithms for Reinforcement Learning
By Csaba Szepesvári
The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations.
10. Reinforcement Learning and Optimal Control
By Dimitri P. Bertsekas
The purpose of the book is to consider large and challenging multistage decision problems, which can be solved in principle by dynamic programming and optimal control, but their exact solution is computationally intractable. We discuss solution methods that rely on approximations to produce suboptimal policies with adequate performance. These methods are collectively referred to as reinforcement learning, and also by alternative names such as approximate dynamic programming, and neuro-dynamic programming.
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