About the book Machine Learning with R, tidyverse, and mlr teaches you how to gain valuable insights from your data using the powerful R programming language. In his engaging and informal style, author and R expert Hefin Ioan Rhys lays a firm foundation of ML basics and introduces you to the tidyverse, a powerful set of R tools designed specifically for practical data science. Oct 24, 2013  'Machine Learning with R' is a practical tutorial that uses hands-on examples to step through real-world application of machine learning. Without shying away from the technical details, we will explore Machine Learning with R using clear and practical examples. Well-suited to machine learning beginners or those with experience.

Books about the R programming language fall in different categories:

• Learning R
• Reference books for the professional R programmer
• Books about data science or visualization, using R to illustrate the concepts

Books are a great way to learn a new programming language. Code samples is another great tool to start learning R, especially if you already use a different programming language. You might also want to check our DSC articles about R: they also include cheat sheets. If you are unsure about learning R, read about R versus Python.

Example of chart produced with R

Books lo learn R

• Learning R - Learn how to perform data analysis with the R language and software environment, even if you have little or no programming experience. With the tutorials in this hands-on guide, you’ll learn how to use the essential R tools you need to know to analyze data, including data types and programming concepts.
• R in a Nutshell - If you’re considering R for statistical computing and data visualization, this book provides a quick and practical guide to just about everything you can do with the open source R language and software environment. You’ll learn how to write R functions and use R packages to help you prepare, visualize, and analyze data. Author Joseph Adler illustrates each process with a wealth of examples from medicine, business, and sports.
• Introduction to Data Science with R - Learn practical skills for visualizing, transforming, and modeling data in R. This comprehensive video course shows you how to explore and understand data, as well as how to build linear and non-linear models in the R language and environment. It’s ideal whether you’re a non-programmer with no data science experience, or a data scientist switching to R from other software such as SAS or Excel.

Reference books

• R Cookbook - With more than 200 practical recipes, this book helps you perform data analysis with R quickly and efficiently. The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression.
• R Graphics Cookbook - This practical guide provides more than 150 recipes to help you generate high-quality graphs quickly, without having to comb through all the details of R’s graphing systems. Each recipe tackles a specific problem with a solution you can apply to your own project, and includes a discussion of how and why the recipe works. Most of the recipes use the ggplot2 package, a powerful and flexible way to make graphs in R. If you have a basic understanding of the R language, you’re ready to get started.
• R Packages - Turn your R code into packages that others can easily download and use. This practical book shows you how to bundle reusable R functions, sample data, and documentation together by applying author Hadley Wickham’s package development philosophy. In the process, you’ll work with devtools, roxygen, and testthat, a set of R packages that automate common development tasks. Devtools encapsulates best practices that Hadley has learned from years of working with this programming language.

Data science books using R for illustration purposes

• A Handbook of Statistical Analyses Using R - Provides a guide to data analysis using the R system for statistical computing. Each chapter includes a brief account of the relevant statistical background, along with appropriate references.
• An Introduction to Statistical Learning: with Applications in R - Provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more.
• Practical Data Science with R - Explains basic principles without the theoretical mumbo-jumbo and jumps right to the real use cases you'll face as you collect, curate, and analyze the data crucial to the success of your business. You'll apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support.
  
• Other Books - 154 books listed on R-Project.org, covering many different fields.

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Additional Reading

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What better way to enjoy this spring weather than with some free machine learning and data science ebooks? Right? Right?

Here is a quick collection of such books to start your fair weather study off on the right foot. The list begins with a base of statistics, moves on to machine learning foundations, progresses to a few bigger picture titles, has a quick look at an advanced topic or 2, and ends off with something that brings it all together. A mix of classic and contemporary titles, hopefully you find something new (to you) and of interest here.

1. Think Stats: Probability and Statistics for Programmers
By Allen B. Downey

Think Stats is an introduction to Probability and Statistics for Python programmers.

Think Stats emphasizes simple techniques you can use to explore real data sets and answer interesting questions. The book presents a case study using data from the National Institutes of Health. Readers are encouraged to work on a project with real datasets.

2. Probabilistic Programming & Bayesian Methods for Hackers
By Cam Davidson-Pilon

An intro to Bayesian methods and probabilistic programming from a computation/understanding-first, mathematics-second point of view.

The Bayesian method is the natural approach to inference, yet it is hidden from readers behind chapters of slow, mathematical analysis. The typical text on Bayesian inference involves two to three chapters on probability theory, then enters what Bayesian inference is. Unfortunately, due to mathematical intractability of most Bayesian models, the reader is only shown simple, artificial examples. This can leave the user with a so-what feeling about Bayesian inference. In fact, this was the author's own prior opinion.

3. Understanding Machine Learning: From Theory to Algorithms
By Shai Shalev-Shwartz and Shai Ben-David

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds.

4. The Elements of Statistical Learning
By Trevor Hastie, Robert Tibshirani and Jerome Friedman

This book descibes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting--the first comprehensive treatment of this topic in any book.

5. An Introduction to Statistical Learning with Applications in R
By Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani

This book provides an introduction to statistical learning methods. It is aimed for upper level undergraduate students, masters students and Ph.D. students in the non-mathematical sciences. The book also contains a number of R labs with detailed explanations on how to implement the various methods in real life settings, and should be a valuable resource for a practicing data scientist.

6. Foundations of Data Science
By Avrim Blum, John Hopcroft, and Ravindran Kannan

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While traditional areas of computer science remain highly important, increasingly researchers of the future will be involved with using computers to understand and extract usable information from massive data arising in applications, not just how to make computers useful on specific well-defined problems. With this in mind we have written this book to cover the theory likely to be useful in the next 40 years, just as an understanding of automata theory, algorithms, and related topics gave students an advantage in the last 40 years.

7. A Programmer's Guide to Data Mining: The Ancient Art of the Numerati
By Ron Zacharski

This guide follows a learn-by-doing approach. Instead of passively reading the book, I encourage you to work through the exercises and experiment with the Python code I provide. I hope you will be actively involved in trying out and programming data mining techniques. The textbook is laid out as a series of small steps that build on each other until, by the time you complete the book, you have laid the foundation for understanding data mining techniques.

8. Mining of Massive Datasets
By Jure Leskovec, Anand Rajaraman and Jeff Ullman

The book is based on Stanford Computer Science course CS246: Mining Massive Datasets (and CS345A: Data Mining).

The book, like the course, is designed at the undergraduate computer science level with no formal prerequisites. To support deeper explorations, most of the chapters are supplemented with further reading references.

9. Deep Learning
By Ian Goodfellow, Yoshua Bengio and Aaron Courville

The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The online version of the book is now complete and will remain available online for free.

10. Machine Learning Yearning
By Andrew Ng Email program for mac and android.

AI, Machine Learning and Deep Learning are transforming numerous industries. But building a machine learning system requires that you make practical decisions:

• Should you collect more training data?
• Should you use end-to-end deep learning?
• How do you deal with your training set not matching your test set?
• and many more.

Historically, the only way to learn how to make these 'strategy' decisions has been a multi-year apprenticeship in a graduate program or company. I am writing a book to help you quickly gain this skill, so that you can become better at building AI systems.

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