An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
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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. 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. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.
Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.
35 reviews for An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
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Carl McQueen –
This book is an amazing resource to get your understanding across many different methods in line. One of the greatest tools of a data scientist and statistician in general is knowledge of best method, or best tool, for a task. Many solutions in data science right now go far too heavily toward one size fits all and this books helps one understand why knowing how to read your results and why to use the method to solve it really, really matter.
Akimon85 –
I recently used this book along with a couple others in a graduate level ML course.
Lilly –
This is a great book. I minus one star since I received a damaged copy. This book has a plastic wrap, hence it is less damaged than another book shipped in the same order.
Terry –
If you know a little statistics and basics of using R / RStudio then this book will be very useful.
Bought it for a Masters course but provides a lot of background for business analytics as well
Mary –
Honestly this book carried me through my statistics masters, it had the perfect detail for this course and covered many of my modules
Mustapha A –
Every chapter is very well explained and at the end of it there is a lab excercise with R which is very helpful.
Youngsuk L. –
This is really a good book. Machine learning is a form of statistical learning and this book provides a great introduction.
Math Customer –
This book is a perfect introduction to statistical learning with a light mathematical approach. It’s easy to read and understand. It is nice to know that Prof. Tibshirani developed the lasso technique, for example, so you are learning straight from the source. I had to study this book for an Actuarial Exam, and I have to admit I enjoyed learning about the subject because of this book. As a supplement, there are videos available on Youtube where the authors go over the topics covered in the book. I’m very thankful for this amazing group of people (James, Witten, Hastie, Tibshirani) that put this admirable work together.
Subramanian. D –
good
Vaibhav gupta –
Good book for beginners who want to learn about machine learning
Arturo Sbr –
I reviewed this book for a class in my master’s program and I loved it from start to end.
I already knew most of the concepts but became hooked because of how clear the explanations are. The authors convey complex ideas with remarkable simplicity, and for that, I think this is the most important book for data scientists.
I am an avid opposer of the R programming language (ew) and even I enjoyed the applied programming parts of the book.
In all honesty, the applications in R are very good, but it’s not the main focus of the book. I think people should read this to understand the inner workings of the most popular AI algorithms instead of learning how to train predictive models (especially in R, haha).
Overall, I think this is a great book for beginners and veterans alike. I would not hesitate to recommend this book to anyone interested in statistics, data and AI.
Emma Peng –
The two professors in the video are the cutest old guy I have ever met!!!
Danielle B. Fonseca –
I like a lot the decision tree with codes
Cielo –
I used this book in my statistical learning & data mining course last summer. At the time, the pdf version of this book was available from my university library so I didn’t get the hard copy until now. The reason I decided to get the hard copy is that the theory/conceptual part is well-balanced between proper depth and easy-to-understand. Even though I’m now doing a Machine Learning training program in Python, I still recall the rationale of different models that were well explained in this book. So I’ve decided to get a permanent copy.
Jakub –
Great book and it’s really worth to buy it as it is much more convenient to jump across different section with a book in your hand than with a PDF.
Ashis Sardar –
First time i got the damaged b&w photo copy of the book from jksellers with 1230/- then after getting refund I do buy it from TheHorizans and this time it’s orginal, satisfied with the book with 1291/-for latest extended 2nd edition
Ruperto Majuca –
I wish they would discuss nonlinear regression vs GAM. Also better if they use caret in the R coding.
Monika Cunningham –
I took a stats class during my masters that used the 1st edition, the additional topics in the 2nd edition make it even better. Fantastic writing, probably the only stats book I’ve read cover-to-cover!
Leonardo Bastos –
Excelente livro introdutório para o aprendizado estatístico. Redefine muito bem conceitos estatísticos tradicionais sob um olhar mais atual no contexto do aprendizado de máquina. Exemplos na linguagem R, o que é ótimo para os alunos “meterem a mão na massa”. Uma referência essencial para estatísticos e cientistas de dados.
Dave –
Absolutely an excellent reading for people with a statistics or basic academic mathematical background.
A great introduction to ML will help to comprehend the big picture of the topic (you can foresee the true complexity and “toughness” of the limitless subject).
Suggested to people interested in Business/Data Analytics and Data Science topics (not for ML engineers).
What I loved is that – at the end of the book – you do come out with a broad but clear understanding of the complexity and general “meaning” of ML.
This can be a great book that helps to clarify if ML is actually a real interest/dream job (and then you need to read more advanced books like “Elements of Stat. Learning” and think of undertaking a deep ML engineering career/academic path).
Or if a more Business Analytics career path (staying on the surface of deep mathematical topics) works better for personal interests, background and career aims.
Mili –
An absolute must read for anyone breaking into machine learning
Jessica Macaluso –
Needed it for class and yep learned things.
Stephen Maharaj –
Practical approach to Statistical Learning. Well written by pioneers in the field.
There is a free pdf and accompanying online course.
Gustavo T. L. Costa –
The authors dedicated their time and knowledge to deliver to the public the best book designed to teach statistics (with the minimum necessary theory) so that machine learning techniques can be understood by the majority of people. Thank you very much for providing us with your knowledge for free (online version). You have taken an important step towards the dissemination of statistical knowledge worldwide.
Mark B. Fernandez –
The authors Hastie and Tibshirani are legends in the stats world, creating GAM and LASSO respectively. Their other textbook “The Elements of Statistical Learning” is geared for PhD students. This textbook is very accessible, with figures and lots of sample code. The target audience is any aspiring data scientist who can learn to code and wants to actually understand what the code/models are doing (but doesn’t need to be able to derive all the original math by hand). In addition to teaching different analyses, this book does a great job on explaining key statistical analysis concepts, like bias vs variance tradeoff, k-fold cross-validation, bootstrapping, finding the right balance in model complexity for your dataset, etc. There is both an R and a Python edition. The 2nd edition includes 3 new chapters on survival analysis, multiple testing, and neural nets. There is a free Stanford MOOC that uses this text.
juan-jose parra-pagan –
Es un buen libro, practico y actualizado, con una version en online de junio 2023, que no cambia la edicion impresa
Obi Von Kenobi –
Explains the fundamentals quite elegantly while keeping the mathematical mechanics simple. Very good on comparing the different learning strategies and error estimatiom and feedback customization. If you are a beginner in this domain then go ahead with this purchase.
Math Customer –
A reader –
Very thorough introduction to machine learning, to be followed by The elements of Statistical Learning, by Hastie Tibshirani James
Ricardo Salas –
wonderfull book, I am currently studying a master in Bionformatics and needed to brush my forgotten lessons of Statistics. Amazed how the authors are able to explain the most advanced and difficult concepts skiping the mathematics below, for example the subject of hyperplanes is so amazingly exposed that it should be given as an role model of teaching and turning a difficult subject into an accesible one.I recommed this book with all my heart¡¡
Dushyant –
Received in excellent condition. Coloured pages, good quality paper and hard cover at a very low price I think. Value for money.
ROHIT SADHU –
the book is best for intermediate people and for people who want to learn in depth the mathematics of machine learning
Revati kumbhar –
Good paper and color print quality.
jupiterrrrr –
good product and best price
AC –
You can find the book PDF online. Nevertheless, you might prefer to read the book version. Excellent book.