An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) Hardcover – 29 September 2017

This book is unparalleled in its coverage of Statistical/Machine Learning. It is comprehensive, easy to understand, and provides numerous examples to aid in comprehension. It also helps develop intuition and serves as a foundation for more mathematically advanced ML topics. Tip: Even if you prefer Python over R, consider using this book for theory and finding other resources for Python implementations.

I am still going through this book but have gone through enough to write a review. The book is really good one to understand the different class of problems and algorithms that we have with data and the predictions you can make with them. The language of the book is very lucid and helps in having a read through quite easily. The other good part is the algorithms and its concepts are discussed in reasonable detail without delving deep into the mathematical proof of the core formula relating to these algorithms which is very good for people who have lost touch with hard core mathematics so to speak. Another very good part is each chapter has an Lab of sorts where it uses R to show examples of how a particular learning model can be put in place with the data sets. All in all it is a self contained book. So just have to install R and you get going into a very interesting journey with data and its learning algorithms. 5 stars from me for this wonderful effort in compiling this book for the authors.

1. ContentThe book is excellent content wise. Good for getting a overview of topics in ML. It would have been better if python was used alongside R, as a personal preference.2. The book is very asthetically pleasing with good print, good quality paper used.3. It is a first edition available for INR 700. The second edition only has one new chapter on deep learning and 20 pages extra in one particular chapter. However the second edition costs approx INR 1700. So I guess its worth buying the first edition at cheap cost and cover the left over pages from pdf.

First off, this is an academic textbook so goes into details of the classical ML algorithms. There is no chapter on neural networks so keep that in mind.That said, solid learning to be found here. It helped me clear a lot of misconceptions on basic stuff as Linear Regression etc.It expects you to know R as its the defacto statistical programming language. I hope they had included Python code somewhere. But, you can always translate the pseudocodes to Python.Printing quality is really good. Just a joy to read and learn from this book.

GREAT BOOK!I WAS DEFINITELY NOT PREVENTED by amazon from posting a negative review about the seller, mentioned at the end. So I just woke up one day and randomly decide to leave these comments because I have nothing better to do. This was a very convincingly copied/pirated version of the original, exactly as expected, low quality print with loose pages crumpled by cheap glue, off brand colours and misaligned text. Over all it's well done and a perfect spot for it would be on your bookshelf or even outside your house across the street in the rain. I am extremely happy that I got scammed with this purchase. If you like getting treated like this (by Welbies Book Store) then they are 100% recommended for you. Love it (not)! Sarcasm? Yes.

This book should be referred by anyone and everyone if they already have built their basic understanding of inferential statistics and Data science realated concepts. This gives you just the right mix of intuitive and mathematical explanation for every concept. However if you are looking for depth of mathematics for the concepts, then Elements of statistical learning might be a better option. This would suit people who are in the intermediate stage of their journey of learning data science related concepts. Irrespective this is a must have book for your book shelf if you are a budding scientist or preparing for one.

The authors are renowned in the fields of statistics and ML. They wrote an extremely engaging book. I really like the trade-offs that are hard to find in many other books. I tried some code but not all of it. Some of the code is fairly cryptic and requires a bit of time to get a hang of it. Overall, it is an authoritative reference on ML. I wish I bought it years ago. Buy it, even if you don't use R.

Pros:1.Concepts are organised neatly2.Impactful presentation with plot and language3.Best at beginner level4.Lab section is super helpful to implement concepts discussed in prev section5.Build good foudation in Statistical LearningCons:1.Since it is aimed to be beginner friendly they don't go deep to each concept mathematics wise . So that they don't confuse or overwhelm reader. This a very minor con as this book not intended for advanced users.