Is introduction to Statistical learning a good book?

To read through the chapters, it’s much more enjoyable than reading other math/stat books, since the ideas behind each model or algorithms are very clear even intuitive, a lot of well-made plots make the understanding even easier. I would like to recommend to anyone who want to enter the world of statistical learning.

Is ISLR enough?

Just mastering ISLR is often enough for data analyst roles. Overall, ESL takes an applied, frequentist approach, as opposed to a Bayesian approach like in the next book. Exercises in this book are not only challenging, but also very useful for individuals generally interested in machine learning research.

Is Elements of Statistical learning a good book?

Top positive review Very comprehensive, sufficiently technical to get most of the plumbing behind machine learning. Very useful as a reference book (actually, there is no other complete reference book). The authors are the real thing (Tibshirani is the one behind the LASSO regularization technique).

What are statistical learning methods?

Statistical Learning is a set of tools for understanding data. These tools broadly come under two classes: supervised learning & unsupervised learning. Generally, supervised learning refers to predicting or estimating an output based on one or more inputs.

How hard is statistical learning?

Statistics is challenging for students because it is taught out of context. Most students do not really learn and apply statistics until they start analyzing data in their own researches. The only way how to learn cooking is to cook. In the same way, the only way to learn statistics is to analyze data on your own.

Why is statistics important in machine learning?

Statistics is a collection of tools that you can use to get answers to important questions about data. Statistics is generally considered a prerequisite to the field of applied machine learning. We need statistics to help transform observations into information and to answer questions about samples of observations.

What is ISLR (an introduction to statistical learning?

An Introduction to Statistical Learning, with Applications in R (ISLR) can be considered a less advanced treatment of the topics found in another classic of the genre written by some of the same authors, The Elements of Statistical Learning.

What do you think about ISL?

“ISL makes modern methods accessible to a wide audience without requiring a background in Statistics or Computer Science. The authors give precise, practical explanations of what methods are available, and when to use them, including explicit R code. Anyone who wants to intelligently analyze complex data should own this book.”

What is the difference between ISLR and ISLR R?

Another major difference between these 2 titles, beyond the level of depth of the material covered, is that ISLR introduces these topics alongside practical implementations in a programming language, in this case R. The book’s preface explicitly addresses the relationship between these 2 texts, as well as potential readership:

What are the best free eBooks to read for Statistics?

After taking a week off, here’s another free eBook offering to add to your collection. This time, let’s check out another classic of the genre, An Introduction to Statistical Learning, with Applications in R, written by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani.