An Introduction to Statistical Learning: with Applications in Python

by Gareth James, Daniela Witten, Trevor Hastie

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, marketing, and 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, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.

Genres: Mathematics, Programming, Textbooks, Artificial Intelligence, Academic, Nonfiction, Technology

607 pages, Hardcover

Published July 1, 2023

Reviews

[TK · Rating 4 out of 5]

It took me 5 months to read this book but I am so happy to have finished it (and the course).

"An Introduction to Statistical Learning" is a solid resource for anyone interested in understanding the fundamentals of machine learning. The lab exercises are particularly helpful—most are easy to follow and provide practical insights into the models discussed. On the other hand, the book has its own library for handling data, which can be a bit confusing if you're used to working with standard tools like Pandas and NumPy.

The mathematical content may sometimes feel dense, but with a bit of effort and some revisiting of core math concepts, it becomes much more "digestible". If you're willing to brush up on the fundamentals, especially math and statistics, you'll find the book's explanation of machine learning model internals more intuitive and accessible.

With the theory (and practice) from this book, I was able to train some ML models (cancer classification blogpost) and put all the knowledge and theory into practice.

For anyone planning to read this book, I recommend following the course on edX (or their own youtube videos) and use google colab ou jupiter notebooks to follow the labs and exercises to practice them. I also recommend brushing up on mathematics and statistics to follow the theory and Python for the labs.

[Amir V · Rating 5 out of 5]

Amazing book. The intersection between statistics and machine learning is a fascinating field, and this book teaches you how even a simple linear regression can have so many intricacies.

[amelia · Rating 5 out of 5]

This book, together with its associated video lectures (free on Youtube - the professors are so nice), the labs, and exercises has built my statistical learning foundation. Excited to apply it to some personal projects.

[Alexandre Caraça · Rating 5 out of 5]

Python counterpart to the widely‑lauded ISL (originally in R), designed to make core statistical learning methods accessible to practitioners and students.

It provides a clear, intuitive treatment of regression, classification, resampling, regularization, SVMs, tree methods, unsupervised learning—and even introduces deep learning and survival analysis—with Python-based labs at each chapter’s end.

Highly approachable and well-structured introduction to statistical learning and machine learning concepts.

Overall, this book is an excellent starting point for anyone looking to build a solid foundation in statistical learning using Python, and it comes highly recommended for its clarity, practical focus, and enduring relevance in the field.

[Mackenzie · Rating 3 out of 5]

It's quite good, but has some major flaws. Notably it includes minimal derivations, and uses a weird, stilted python environment with its own dedicated package, rather than simply teaching the reader how to configure Python environments and load data on their own. I would suggest supplementing this book with documentation relevant to a popular statistical learning package (I won't suggest one in case the new hotness has changed by the time you read this), and perhaps a more rigorous statistical text. Perhaps start with the probability theory section of statlect.com ? Or just read elements of statistical learning I guess.

[Navid Ghorbanali · Rating 5 out of 5]

A masterpiece, the first book I would recommend to somebody trying to understand and apply machine learning algorithms.

It does a phenomenal job of introducing the key ideas behind the practices, the strengths and limitations of each model, with a focus on what you need to know, only lightly covering the more abstract details wherever necessary.

Of course, those interested in the topic at a deeper, more theoretical level without emphasis on application should seek out the equally famous Elements of Statistical Learning, or see Kevin Murphy's two books 'Probabilistic Machine Learning'.

Nevertheless, for an introductory/applied book, there is nothing better.

[Jesse Hamill · Rating 5 out of 5]

Perfect level of detail when explaining theory and plenty of examples in python of how to apply. There were good explanations for explaining bias-variance trade off when creating a model; bootstrapping and its application to bagging methods; generalised linear models; gradient boosting models; neural networks. Available as a free online pdf. A Jupyter notebook is available to test out the methods taught in the book.

[GaidenSpence · Rating 5 out of 5]

A very introduction friendly book about the machine learning algorithms and the techniques that can be used on data sets. What really helps is using python for problem sets to go along with the concepts. Great book would recommend.

[Osian Shelley · Rating 4 out of 5]

Good overview of SL techniques with excellent associated Python notebooks.

[d · Rating 5 out of 5]

The masterpiece

[Mushfikur R. Mahi · Rating 4 out of 5]

It's an awesome book for those who want to get into machine learning. It has been covered from basic to advanced and also has practical information

[Camden Polley · Rating 4 out of 5]

Great content, feel like it covered anything I could’ve asked for. Purposefully doesn’t go very deep but has a bunch of great Python data sets and demos that can be played around with.