An Introduction to Statistical Learning with Applications in R

Understand how to make sense of data with this amazing introduction to statistical learning!

An introduction to statistical learning with applications in R

The following is a review of the book An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani.


Data and statistics are an increasingly important part of modern life, and nearly everyone would be better off with a deeper understanding of the tools that help explain our world. Even if you don’t want to become a data analyst, Machine learning engineer, or Data Scientist―which happen to be some of the fastest-growing jobs out there―this book is an invaluable guide to help explain what’s going on.

An Introduction to Statistical Learning 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.

It presents the perfect introduction to the intersection between statistics and machine learning, covering topics that go from the most basic like linear regression to more advanced like Support Vector Machines and clustering techniques. Its contents provide a great se of tools for data analysis and predictions, including the most common Machine Learning algorithms except Artificial Neural Networks: Regressions, Logistical Regression, Linear discriminant analysis, Decision Trees, Random Forest, Boosting, Cross Validation, SVM, PCA, K-means clustering and more.

As the goal of the book, aside from teaching the main concepts behind these techniques is to present them in a practical and applicable manner, each chapter contains a tutorial on implementing the analysis methods and prediction techniques in the R programming language. While in this blog we normally recommend Python as the de-facto programming language for Data Science, R offers some great analysis tools and statistical methods that are hard to find with any other language/framework.

Also, at the end of each chapter there are exercises that cover both, the theoretical and practical parts of what has been covered, and that are a perfect (and sometimes challenging) way to test our working knowledge of what has been taught up to that point. We profoundly recommend dedicating some time to slowly and properly completing these exercises.

Overall, the book offers a clear application of the Mathematics and application of the R programming language to statistical learning, with fantastically written, beautiful explanations of each topic, that requiere a solid mathematical background. Later in this article, we will cover in depth who this book is oriented to.

Two of the authors of An Introduction to Statistical Learning, are authors of the famous text The Elements of Statistical Learning (The Bible of Machine Learning). Because of the more advanced character of that book, many people think that An Introduction to Statistical Learning is a good precursor for the former book, however, from our point of view this is not true. Both books cover similar topics, however, An Introduction to Statistical Learning does so in a much more accesible manner, making the book great fro a much broader audience. For people with a very high level of math then The Elements of Statistical Learning might be the preferred way to dig deeper into some topics, but for those that do not have a great mathematical background then An Introduction to statistical learning is probably a better option.

If you are not a mathematician or have a Bachelor or PHD in Mathematics, and you just need to apply data analytics to your research or in your job, this book will really help you.


The contents of the book are the following:

  1. Introduction
  2. Statistical Learning
  3. Linear Regression
  4. Classification
  5. Resampling Methods
  6. Linear Model Selection and Regularization
  7. Moving Beyond Linearity
  8. Tree-Based Methods
  9. Support Vector Machines
  10. Unsupervised Learning

On the following link you can find the official Springer website of the text.

Also, most of the contents covered in this book can be found on Youtube on the StatLearning video series by the same authors. On the following video you can find the first lesson, with an Introduction of statistical learning, how it has evolved, and its relationship with Machine Learning.

Who is this book for?

In general, this book is oriented to those who wish to use cutting-edge statistical learning techniques to analyse and leverage the power of their data, requiring only the maths that can be provided by any STEM university degree. If you have that math background cover, then this text is a great Introduction to statistical learning.

We think this book is best for those with a Computer Science background, that are already implementing Machine Learning algorithms and models, that want to step up their understanding of the underlying theory behind them. It is a great continuation of many introductory Machine Learning courses, as it will allow you to deepen your knowledge and consolidate what you already have learned.

Many people want to go into the field of Machine Learning in a very fast paced manner, by learning straight away how to code and implement algorithms. For use, while it migth be good to start by programming to asses if you like it and are interested in further learning, it is also essential that once this has been covered you obtain a strong statistical base, which this book is perfect for.


An Introduction to Statistical Learning is great book to learn the basis of Machine Learning, if picked up by the right audience. If you are looking to obtain a profound statistical knowledge of the concepts underlying machine learning algorithms, but don’t want to go into a super heaving mathematical text like The Elements of Statistical Learning, then this book is an awesome candidate. It will give you a very strong foundation and a great understading of the statistics of Machine Learning, and a great idea of how to implement the algorithms using R.

It is one of the best Machine Learning books to start going deep into the theory, and is also great to for those in the business of mathematics and statistics.

Level up on your Data Skills with ‘An Introduction to Statistical Knowledge’! You can find the book on amazon at the best price here:

An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
  • Hardcover Book
  • James, Gareth (Author)
  • English (Publication Language)
  • 440 Pages - 06/25/2013 (Publication Date) - Springer (Publisher)

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