An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
Product ID: 736755542
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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. Review: Good - It was the same thing I ordered for Review: Classic book - You can find the book PDF online. Nevertheless, you might prefer to read the book version. Excellent book.
| Amazon 売れ筋ランキング | 洋書 - 43,220位 ( 洋書の売れ筋ランキングを見る ) Mathematical & Statistical Software - 28位 Data Mining - 36位 Theory of Computing - 55位 |
| おすすめ度 | 5つ星のうち4.6 447 レビュー |
E**H
Good
It was the same thing I ordered for
A**C
Classic book
You can find the book PDF online. Nevertheless, you might prefer to read the book version. Excellent book.
名**生
商品の状態が...
カバーの一部が破けていました(ハードカバーなのに...)
A**ー
セカンドエディション買ったのにボロボロの2013年版がインドから届いた
セカンドエディション買ったのにボロボロの2013年版がインドから届いた
M**Z
The Most Accessible Statistics Textbook
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.
R**S
a MUST reading
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¡¡
E**G
Very helpful for non-statistic beginners, remember to learn it with their Videos!
The two professors in the video are the cutest old guy I have ever met!!!
P**I
Best book to get into theoretical aspects of ML
Book is amazing if you want to dip your toes into how things work inside different ML models. It has just enough maths that you can understand everything if you have cleared JEE main with a good percentile. The content can help you clear most ML interviews, except maybe the ones for the very top companies. Although if you want more mathematical perspective try its older cousin Elements of Statistical Learning
A**R
Greatest Data Science book ever (coming from someone who hates R)
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.
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