Introduction to Statistical Learning book | what is machine learning and Types? Basics | Explained

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An Introduction to Statistical Learning
Authors: Gareth James • Daniela Witten • Trevor Hastie • Robert Tibshirani

Why read this book?
One of the first books in this area—The Elements of Statistical Learning (ESL) (Hastie, Tibshirani, and Friedman)—was published in 2001, with a second edition in 2009.

ESL has become a popular text not only in statistics but also in related fields. One of the reasons for ESL’s popularity is its relatively accessible style. But ESL is intended for individuals with advanced training in the mathematical sciences. An Introduction to Statistical
An Introduction to Statistical Learning (ISL) arose from the perceived need for a broader and less technical treatment of these topics
In this new book, we cover many of the same topics as ESL, but we concentrate more on the applications of the methods and less on the mathematical details.

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An Overview of Statistical Learning
Statistical learning refers to a vast set of tools for understanding data.
These tools can be classified as supervised or unsupervised.

Broadly speaking, supervised statistical learning involves building a statistical model for predicting,
or estimating, an output based on one or more inputs.
Problems of this nature occur in fields as diverse as business, medicine, astrophysics, and
public policy.

With unsupervised statistical learning, there are inputs but no supervising output; nevertheless, we can learn relationships and structure from such data.

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