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Preview — An Introduction to Statistical Learning by Gareth James
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..more
Published September 1st 2017 by Springer
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Jan 30, 2016afloatingpoint rated it it was amazing
Excellent book! Jan 27, 2014Josh Davis rated it it was amazing
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..more
Shelves: owned-read-books, textbooks, owned-cs-books, machine-learning, textbooks-spring-2014
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.
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.
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..more
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..more
Excellent book for anybody that wants to start adventuring in the marvelous world of data science
Jan 03, 2016Shalini rated it it was amazing
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..more
Oct 14, 2014Ji rated it liked it
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.
Nov 09, 2016Vysloczil rated it it was amazing
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..).
Mar 17, 2019A.N. Mignan rated it it was amazing
An excellent introduction to statistical learning presenting the main algorithms for both regression and classification (linear regression, logistic regression, lasso, LDA, KNN, tree bagging and boosting, SVM, etc), as well as the important statistical tests (R^2, p-value, ROC, CV, concept of bias-variance tradeoff, etc..). Things are kept very simple with light-weight mathematics. The accompanying R labs help the reader consolidate his knowledge and get his hands dirty on real datasets. The ex..more
Oct 15, 2018Metin Ozturk Ozturk rated it it was amazing
One of the best introductions to Machine Learning.
One of the finest intro ML books of our times.
Authorative but very equation heavy. I read three chapters then stopped as I had enough info from those to expand my knowledge.
Feb 15, 2014Jerzy marked it as to-read
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. Mar 18, 2018Lord_Humungus rated it really liked it
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..more
Recommends it for: beginners in statistics after their first course
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..more
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..more
Oct 20, 2014Truc-Vien Nguyen rated it it was amazing
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.
Nov 08, 2016Michael Regier rated it really liked it
Feb 20, 2018Thanh Tùng rated it it was amazing
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..more
Oct 24, 2017Bhashit Parikh rated it it was amazing
Shelves: math, statistical-learning, statistics, programming
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.
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.
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.
A very well edited book that helped me (a non-statistician) better understand what methods can be used (and not used) to derive insights from data. Commendable the practical demonstrations using an open-source statistical software (R). One can skip many of the theoretical demonstrations and still come out feeling that we have learned something.
Nov 19, 2018Ulises Jimenez rated it really liked it
It contains explanation to the main statistical methods used in Machine Learning. It requires some mathematical and statistical background to follow up. The only reason why I rate it 4, it is because it uses R instead of Python for the examples.
Aug 08, 2017Rrrrrron rated it it was amazing
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.
Feb 10, 2018Alexandru Tudorica rated it it was amazing
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.
Jun 05, 2017Alex rated it it was amazing
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.
More complex and focused on proofs than Machine Learning with R by Brett Lantz. Great as a supplement reading for the introductory machine learning course taught at MFF in Prague.
Intuitively explains model interpretation. R examples are easy to follow and the rationale behind each step is clear.
Compact. Can learn a few things in R quickly.
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