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 techniqu 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, 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.

# An Introduction to Statistical Learning: With Applications in R

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 techniqu 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, 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.

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## 30 review for An Introduction to Statistical Learning: With Applications in R

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4out of 5afloatingpoint–Excellent book! The book explains concepts of Statistical Learning from the very beginning. The core ideas such as bias-variance tradeoff are deeply discussed and revisited in many problems. The included R examples are particularly helpful for beginners to learn R. The book also provides a brief, but concise description of functions' parameters for many related R packages. My professor thinks this book is a "superficial" version of The Elements of Statistical Learning, but I disagree. Yes, it may Excellent book! The book explains concepts of Statistical Learning from the very beginning. The core ideas such as bias-variance tradeoff are deeply discussed and revisited in many problems. The included R examples are particularly helpful for beginners to learn R. The book also provides a brief, but concise description of functions' parameters for many related R packages. My professor thinks this book is a "superficial" version of The Elements of Statistical Learning, but I disagree. Yes, it may be easy for the reader to understand, but isn't it true that a great educator is someone who can explain complicated concepts in simple terms? There should be no shame in reading such a book, one that does a wonderful job of breaking things down. If one wishes to learn more about a particular topic, I'd recommend The Element of Statistical Learning. These two pair nicely together.

5out of 5Josh Davis–I took a Machine Learning class during my last semester. This is the book that was used for the course (we also used Elements of Statistical Learning as the secondary text). I loved it. I thought the explanations were great as well as the exercises. I took the online course offered through Stanford at the same time and got to watch Trevor Hastie & Rob Tibshirani themselves. The videos were hilarious and informative. I'd highly recommend reading the book as well as taking the online course.

5out of 5Eric–Clear, intuitive exposition of a subset of methods in statistical learning. Great illustrations and plenty of R code. My only complaint is that the R code is quite ugly looking, which is no surprise since it was written by statisticians, but the authors should be forgiven for this minor infraction. Overall I highly recommend this book.

5out of 5Marco–A good introduction to the methods of statistical learning, presenting techniques in a clear way and showing some of the practical issues involved in real-world use of regression and classification models. While some math is unavoidable when defining the tools presented in this book, the formulas are kept at a level that might be suitable for those with less mathematical baggage than willingness to understand the concepts, and the R exercises can be very useful to the more practically-minded rea A good introduction to the methods of statistical learning, presenting techniques in a clear way and showing some of the practical issues involved in real-world use of regression and classification models. While some math is unavoidable when defining the tools presented in this book, the formulas are kept at a level that might be suitable for those with less mathematical baggage than willingness to understand the concepts, and the R exercises can be very useful to the more practically-minded readers.

5out of 5Stephen Lung–Amazing book! A great intro to ML and statistical learning with some solid, clear and practical examples. Some of the concepts introduced appear so simple to the human mind, but getting the machine to learn these concepts is a whole different science. This book made me appreciate the wonders of ML. It also reinforced the notion that vast industries will be revolutionized, it is just a matter of time. In this book alone, I learned about the different techniques in supervised learning and unsuperv Amazing book! A great intro to ML and statistical learning with some solid, clear and practical examples. Some of the concepts introduced appear so simple to the human mind, but getting the machine to learn these concepts is a whole different science. This book made me appreciate the wonders of ML. It also reinforced the notion that vast industries will be revolutionized, it is just a matter of time. In this book alone, I learned about the different techniques in supervised learning and unsupervised learning algorithms (ie. Bootstrap, bagging, random forest, boosting). - Bootstrap: technique to treat the sample as a population and repeatedly drawing new samples - Bagging: averaging the resulting predictions from X # of decision trees - Random Forest: As decision trees are split, only strong predictors are kept forcing the average to be less variable - Boosting: Each decision tree is built using information from old trees, so all the trees are a modified version of the initial dataset It's my first step into data science but won't be my last on it. Looking forward to deepening my knowledge into this. Reading the book + watching the ISL youtube vids helped significantly in understanding the concepts.

4out of 5Shalini–The book starts with a good introduction to basic classifiers, their differences, why we need each one of them or why we don't. It also mentions evaluators for each kind of classifier and explains how they are relevant in the beginning chapters. This is extremely helpful since it provides a holistic view of the flow which will be explained in further chapters. Much better intro to machine learning compared to other books. Loads of problems to work on which makes sure the understanding has seeped The book starts with a good introduction to basic classifiers, their differences, why we need each one of them or why we don't. It also mentions evaluators for each kind of classifier and explains how they are relevant in the beginning chapters. This is extremely helpful since it provides a holistic view of the flow which will be explained in further chapters. Much better intro to machine learning compared to other books. Loads of problems to work on which makes sure the understanding has seeped in.

4out of 5Vysloczil–Probably the most accessible machine/statistical learning textbook out there. Even understandable for people without rigorous training in statistics or mathematics. Very much based on intuition. Pay attention to the videos by the authors that follow the chapters of the book (made for a Stanford MOOC but freely accessible on yt: https://www.r-bloggers.com/in-depth-i...).

4out of 5Ji–A great book to get started with basic theory behind statistical learning methods. I have to admit that I went through the book in a rush and barely spent enough time to cover the whole book. It's going to be worthy of a revisit in the future per I jumped into quick questions in some theoretical foundations. Good for anybody who wants to pick up machine learning theories using R, with limited or little prior knowledge in both fields.

5out of 5Metin Ozturk Ozturk–One of the best introductions to Machine Learning.

4out of 5Marcus–Authorative but very equation heavy. I read three chapters then stopped as I had enough info from those to expand my knowledge.

4out of 5Jerzy–Skimmed just through Ch 3 (linear regression) so far. Hoped it'd be something I can recommend to a total novice, but it isn't. That's fine---it's just for a higher-level audience than I was hoping. Based on my experience TA'ing statistical novices, I suspect the linear regression stuff is already too dense and rushed to help them really understand what's going on & why. They'll need a little more time on each aspect, a few more examples, a little deeper sense of why we do these things. On the Skimmed just through Ch 3 (linear regression) so far. Hoped it'd be something I can recommend to a total novice, but it isn't. That's fine---it's just for a higher-level audience than I was hoping. Based on my experience TA'ing statistical novices, I suspect the linear regression stuff is already too dense and rushed to help them really understand what's going on & why. They'll need a little more time on each aspect, a few more examples, a little deeper sense of why we do these things. On the other hand, if you've already taken a regression course, this chapter is a perfectly good review. Also, pet peeve: while this book is aimed mostly at prediction (and that's great), they also chose to mention hypothesis testing briefly and didn't handle it well. Yes, statistical hypotheses are *phrased* as questions about parameters (say, is the regression slope beta1 = 0?). But they're really questions about the *dataset*, its design and size. The actual question isn't "Is the slope really exactly 0?" but rather "Did we *measure* the slope precisely enough?" If we don't reject the null, that doesn't mean we conclude something is zero. We conclude we don't have enough data to estimate it precisely, and that opens a distinct set of options: * collect more data to estimate it better; or * drop it from the model because the estimate is noisy even though it could have an important effect, to avoid overfitting; or * include it in the model even though the estimate is noisy, to avoid underfitting. We should be making it clear to students that we have a real decision between these options. If we sweep it under the rug of "always drop insigificant terms," we weaken their understanding and we lose the important connections with bias-variance tradeoff, over- vs under-fitting, etc.

4out of 5Joe Suzuki–I used this book for my course (undergraduate math dept) at Osaka University, a top-five university in Japan. The book is written in English and few students read the book while I explained the contents in Japanese in the class. I found the presentation including many figures and excluding equations (the discussion is mathematically sound) is very impressive and rather comfortable. I really recommend to read the book first rather than "Elements of Statistical Learning ". (Currently, I am too busy I used this book for my course (undergraduate math dept) at Osaka University, a top-five university in Japan. The book is written in English and few students read the book while I explained the contents in Japanese in the class. I found the presentation including many figures and excluding equations (the discussion is mathematically sound) is very impressive and rather comfortable. I really recommend to read the book first rather than "Elements of Statistical Learning ". (Currently, I am too busy to write a complete review but will continue to update the above text.)

4out of 5Truc-Vien Nguyen–Quite solid, clear and practical for statistical learning, but also easy to understand. I got a kindle edition and used it as reference book. It covers main topics in statistical learning methods, from statistics for complex datasets, yet not require readers to have a strong mathematical background.

5out of 5Lord_Humungus–A very good book of statistics that you can read after your Statistics 101 course, centered on machine learning. Very clear prose, very consistent notation, and in general everything that one asks from a good statistics book. I've read 95% of it and it's very good if you don`t know much. I found the exercises quite difficult, though. I have no knowledge of algebra or calculus, so I just could't do some of them. And many things I had to believe by faith. I'm ok with faith, but ocassionally the au A very good book of statistics that you can read after your Statistics 101 course, centered on machine learning. Very clear prose, very consistent notation, and in general everything that one asks from a good statistics book. I've read 95% of it and it's very good if you don`t know much. I found the exercises quite difficult, though. I have no knowledge of algebra or calculus, so I just could't do some of them. And many things I had to believe by faith. I'm ok with faith, but ocassionally the authors dug deeper and I became lost. But only 5% of the time. The good thing is that after you read it, you can do pretty cool things with R, thanks to the labs at the end of each chapter. I also liked the great importance the authors give to resampling methods (basically the way they test their models) and to the variance-bias tradeoff. Excellent second book for beginners. The non-simplified version (Elements of Statistical Learning) I found too advanced and unreadable from the first page.

5out of 5Thanh TÃ¹ng–A great introduction book for statistical learning, a closely related field to machine learning. This is the accompany book for the course with the same name by Stanford University online MOOC platform. The content is intended for the beginners in machine learning therefore much less math than the other book - Element of Statistical Learning. The professors are actually the authors of several most popular algorithms (boostrap, ridge and lasso regression,...) and many R packages that we are using A great introduction book for statistical learning, a closely related field to machine learning. This is the accompany book for the course with the same name by Stanford University online MOOC platform. The content is intended for the beginners in machine learning therefore much less math than the other book - Element of Statistical Learning. The professors are actually the authors of several most popular algorithms (boostrap, ridge and lasso regression,...) and many R packages that we are using nowadays so it's interesting to understanding data science from a historical perspective. The lessons are inspiring and the instructors are sometimes very fun. One of the best online course I have ever completed and I would highly recommend this book and the course for anyone who wants to start studying statistics and machine learning

4out of 5Bhashit Parikh–Probably the best book on statistical learning for beginners. Explanations are intuitive and don't get bogged down in too much detail, which is a plus for an introductory book. The R exercises are great. By the end of the book, I feel like I have a solid foundation for statistical learning. To be followed by The Elements of Statistical Learning, which offers a lot more background and details about these statistical learning methods.

5out of 5Ferhat Culfaz–Phenomenal book. Though it has examples in R, this can easily be translated to Python or Matlab. More importantly, the description, diagrams and examples in the book for the various statistical learning techniques are the best I have seen anywhere. Very clear, concise and up to the point with excellent examples. This book is the simpler, more accessible version to Elements of Statistical Learning. Indispensable.

5out of 5Xingda Wang–To be honest, I don't think it's a "Introduction" book, using R is good, but the parameters explanation are missing, which makes it really difficult to understand, I start several statistics courses, 2 in campus, and 3 online, but still have difficult to get the point. I read the book 2 times with UNFINISHED and STOPED at chapter 4 twice.

4out of 5Rrrrrron–Excellent intro for someone coming with a background in math or a field where they have learned measure theory and optimization theory. The figures are great for providing intuition for how each of the methods work and how they perform. The R exercises are just ok.

5out of 5Alexandru Tudorica–Packed with useful information and practical tips, without the usual proof clutter associated with rigorous statements. Very good as a quick and thorough reference. Haven't worked out through the exercises since they're focused on R and I'm a Python person.

4out of 5Alex–Very clear presentation of a broad range of fundamental ML concepts; definitely useful regardless of whether or not you use R. Up there with the excellent "Data Science for Business" by Fawcett and Provost in terms of breadth of coverage and quality of explanations.

4out of 5Jasmine–Intuitively explains model interpretation. R examples are easy to follow and the rationale behind each step is clear.

5out of 5Kaniballos–Superb introductory material .

5out of 5Jason Copenhaver–What I read was good. But my enthusiasm for the subject has waned. Maybe I'll come back to it eventually.

5out of 5Oleksandr Bilyk–This is my first "Springer" publisher book...

5out of 5Josue–Great way to get into analyzing data sets with the R programming language.

5out of 5Chunduri Balaji–good book. need to read again to get better clarity of concepts

5out of 5Ben–helpful for applying ml+stats in r

4out of 5Jordan–good introductory text to data science and ML and other good stuff

5out of 5Jorge–This is a great introductory book. However, I did not give a 5 star because there were a lot of missing steps, even for a beginners book, that I had to look for in other places.