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

Without any suspense, "An Introduction to Statistical Learning" (ISL) by James, Witten, Hastie and Tibshirani is a key book in the Data Science literature. I would summarize it as a book written by statisticians for non-statisticians. Indeed, while the book "The Elements of Statistical Learning" was heavy on theory and equations, ISL is the practical counterpart. The book is very clear and contains only theory you need to understand the data mining algorithms covered. It's thus a invaluable resource for Data Scientists who don't need all theorems and proofs related to a given algorithm, but still need to understand how it works. Several examples are provided to illustrate each algorithm. Each chapter contains a section with R labs, showing the code needed to move from reading the book to doing data science. The book has a strong emphasis on linear regression and related non-linear approaches (more than half of the book). This lets very few place to other approaches such as decision trees and SVM, which are still covered. The final chapter rapidly covers PCA and clustering. Although the book is targeted towards a larger audience than statisticians, you shouldn't be afraid of equations (by the way, if you look for an excellent book covering data science algorithms with nearly no equation, have a look at "Data Science for Business" from Provost and Fawcett). With such an excellent book, we are obviously more exigent and I was looking for more coverage of validity indices for clustering, Support Vector Regression, and a final chapter about trends and challenges. In conclusion, ISL is the definitive resource for Data Scientists who want to get the correct level of statistical background in their work.

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.

Received in good condition.

Wie schon hier erwähnt, ist An Introduction to Statistical Learning (ISL) eine ausgezeichnete Einführung ins Machine Learning. Man kann es als den kleinen Bruder von "The Elements of Statistical Lernen" (ESL) sehen. Es werden alle relevanten Themen vom Statistical Learning/Machine Learning (classification, clustering, supervised, unsupervised, usw.) in wenigen Seiten behandelt. Denn ISL ist extrem gut erklärt und benutzt eine einfache Sprache. Wenn man noch zusätzlich das Stanford MOOC "Statistical Learning" belegt, bekommt man eine sehr fundierte Basis. ISL verzichtet auf komplexe mathematische Beweise und ist tatsächlich als Anwendungsbuch zum Selbstlernen gedacht. Es wird keine großen Vorkenntnissen erwartet. Man lernt anhand von der Programmiersprache R, wie man Datensätze analysiert und Zusammenhänge vorhersagen kann. Wenn man noch über die Theorie dahinter erfahren möchte, oder tiefer ins Thema gehen will, ist ESL bestens empfohlen. Hier machen die Autoren sehr deutlich, dass ISL sich um ein Praxisbuch handelt. Das Buch ist auch sehr schön und hochwertig gemacht (man merkt es an den Preis). Die Seiten sind bunt und aus qualitativem Papier. Der Preis ist außerdem komplett gerechtfertigt, da es einem sofort klar wird, wie viel Zeit und Leidenschaft die Autoren investiert haben, um ein konzises aber präzises Fachbuch zu konzipieren. Es handelt sich um ein extrem didaktisches Buch und leider ist dies in der Informatik/Mathematik häufig eine Seltenheit. Man kann das Buch kostenlos (und legal) im Internet als PDF herunterladen. Die Autoren haben es zur Verfügung gestellt. Es lohnt sich aber zum Kauf. Denn die Autoren haben es sehr wohl verdient. ISL wird wie ihr Großbruder zum Standardwerk und es kann jedem empfohlen werden, der ein Interesse an dem Thema hat.

This is a great book if you want to learn the basics of statistical analysis and how to apply these methods in R. If you're an absolute beginner then you might get a little confused, but it's all written in simple terms and does a good job of avoiding unnecessary terminology.

This book is quite fantastic for an introductory level. I have a specialised training in mathematics and this books puts me up to date on several topics and algorithms without having to deal much time in the mathematical technicalities (for that, there are specialised treatises on each subject). The R code is quite good and although I think some of their advice is not appropriate (e.g. the attach function is in R only for legacy purposes but shouldn't be used since it overrides other functions, including base, aka default, functions), it is workable and so it's a great investment. The book is introductory focusing mostly on intuition and how to do, little time is spent in mathematical formalities, so that's a plus. I think there is a NEWER edition than the one I reviewed but otherwise I suspect the newer edition will be just better. Recommended.

El libro perfecto para aquellos que deseen iniciarse en el mundo del data science. Se centra en explicar los conceptos de manera clara, omitiendo desarrollos matemáticos. Lo único que echo en falta son las soluciones a los problemas propuestos el final de cada capítulo