An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) 1st Edition

This is a wonderful book for an intro to the world of statistical learning. As an engineering students, it is very approachable and readable. It took me 2 days to finish all chapters, without exercise. To read through the chapters, it's much more enjoyable than reading other math/stat books, since the ideas behind each model or algorithms are very clear even intuitive, a lot of well-made plots make the understanding even easier. I would like to recommend to anyone who want to enter the world of statistical learning.However, from a graduate level student, I would say this book is more suitable for a undergrad stat or related field student, practitioners, or an entry level graduate student who is not majoring in stat or math. The ideas are much more intuitive than rigorous. If only use such book to do any real world problem, even though they talk about cross validation or something a little bit involved, practitioners may either came across so much problems in statistical analysis, or come to a wrong conclusion. Not saying the methods within this book is wrong, but without deep understanding of some theories or rigorous assumpions of the methods, pure blind trying different algorithms to find lowest MSE may not be suitable for some cases.Still, this is a wonderful book for two cases:1. If you have some background in theoretical or mathematical statistics and want to gain some knowledge of applied methods, this book will be wonderful for you to find applications with your theoretical knowledge;2. If you have few knowledge about rigorous statistics, but want to enter the world of statistical/machine learning, this one is very suitable to trigger your interest for reading deeper and more rigorous books, such as ESL.For myself, this books is more like a ticket. I have the ticket of a beautiful state park. I use it to cross the gate of the park, but stand near the gate to give an overlook of the beautiful scenes of the park. The map described on the ticket is only contained the main road of the park. If you want to check more beautiful scenes, you need more work, more tickets, more tools to take an adventure within this park for quite a while.

If you want to build a comprehensive machine learning library, this would be the first book to purchase. While it does cover all of the basics, it is not watered down by any means. (I had the same fear as BK Reader) I found the following to be especially helpful;1. Straight talk - These experts come right and say which methods work best under which circumstances. While there are many fancy algorithms covered in the book, they highlight the advantages of the simpler ones.2. Emphasis on subjects that are not heavily addressed in most ML books - They thoroughly cover the challenges of high-dimensionality, data cleaning, and standardization. They do not limit their attention to these subjects to one chapter. They bring them up continually throughout the book.3. Expertise - Dr. Hastie and Dr. Tibshirani are two of the thought leaders in statistical learning. You can be assured that you are learning from the best.4. Many levels of depth - While the book does cover the basics, it is not watered down by any means. (I had the same worry as BK Reader) There is a great deal for any student of statistics; beginner or advanced.5. R code - You are given enough code and examples to gain confidence in your ability to independently perform excellent analysis and modeling.6. The concepts are just plain exciting! - You will feel an excitement as you discover and re-discover the algorithms they present.The book is a standard work along with Elements of Statistical Learning and Pattern Recognition and Machine Learning (the Bayesian approach). If you enjoy the book, you may also want to consider Applied Predictive Modeling. It has the same style and approach.

The book provides the right amount of theory and practice, unlike the earlier (venerable and, by now, stable) text authored (partly) by the last two authors of this one (Elements of Statistical Learning), which was/is a little heavy on the theoretical side (at least for practitioners without a strong mathematical background). The authors make no pretense about this either. The Preface says "But ESL is intended for individuals with advanced training in the mathematical sciences. An Introduction to Statistical Learning (ISL) arose from the perceived need for a broader and less technical treatment of these topics."ISL is neither as comprehensive nor as in-depth as ESL. It is, however, an excellent introduction to Learning due to the ability of the authors to strike a perfect balance between theory and practice. Theory is there to aim the reader as to understand the purpose and the "R Labs" at the end of each chapter are as valuable (or perhaps even more) than the end-of-chapter exercises.ISL is an excellent choice for a two-semester advanced undergraduate (or early graduate) course, practitioners trained in classical statistics who want to enter the Learning space, and seasoned Machine Learners. It is especially helpful for getting the fundamentals down without being bogged down in heavy mathematical theory, a great way to kick-off corporate Learning units, or as an aid to help statisticians and learners communicate better.A needed and welcome addition to the Learning literature, authored by some of the most well respected names in industry and academia. A classic in the making. Recommended unreservedly.____________________________________________UPDATE (12/17/2013): Two of the authors (Hastie & Tibshirani) are offering a 10-week free online course (StatLearning: Statistical Learning) based on this book found at Stanford University's Web site (Starting Jan. 21, 2014). They also say that "As of January 5, 2014, the pdf for this book will be available for free, with the consent of the publisher, on the book website." Amazing opportunity! Enjoy!____________________________________________UPDATE (04/03/2014): I took the course above and found it very helpful and insightful. You don't need the course to understand the book. If anything, the course videos are less detailed than the book. It is certainly nice, though, to see the actual authors explain the material. Also, the interviews by Efron and Friedman were a nice touch. The course will be offered again in the future.