Name: An Introduction To Statistical Learning With Applications In R
ISBN: 9781461471370

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 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.

What Hacker News says

rramadassDec 2024

I am no expert, but i found the following really helpful to understand AI/ML from the basics;

1) Neural Networks for Applied Sciences and Engineering by Sandhya Samarasinghe. Old book but great for understanding.

2) An Introduction to Statistical Learning: With Applications in R or Python by Hastie, Witten et al.

3) Any Statistics and Probability textbook should be at hand for reference.

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wavemodeDec 2023

ISLR (aka "An Introduction to Statistical Learning: With Applications in R") is a great book on the principles of machine learning (including regression). And you get practical experience using R to actually implement such applications.

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rramadassDec 2023

1. Get An Introduction to Statistical Learning with Applications in R/Python (aka ISLR/ISLP) by Hastie et al. Read this from cover-to-cover and make sure that you understand the concepts/ideas/nuances/subtleties explained.

2. Keep a couple of Mathematics/Statistics books handy while you are going through the above. When the above book talks about some Maths technique you don't know/understand you should immediately consult these books (and/or watch some short Youtube videos) to grasp the concept and usage. This way you learn/understand the necessary Mathematics inline without being overwhelmed.

This is the simplest and most direct route to studying and understanding AI/ML. Everything else mentioned in this thread should only come after this.

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staredJul 2020

While there are some good picks (especially "An Introduction to Statistical Learning with Applications in R" and "Deep Learning with Python" by Chollet, the Keras author), I am surprised it is missing "Information Theory, Inference, and Learning Algorithms" by David MacKay (http://www.inference.org.uk/itila/book.html).

The "Bayesian Inference and Machine Learning" track gives a nice foundation to anything with "log loss". After that even k-means won't be an ad-hoc algorithm.

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curiousgalJul 2018

The Elements of Statistical Learning

or the more beginner friendly

An Introduction to Statistical Learning: With Applications in R

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mswenNov 2017

Introduction to Statistical Learning with Applications in R http://www-bcf.usc.edu/~gareth/ISL/

Statistical Rethinking by Richard McElreath gives a good introduction to Bayesian approaches to statistical analysis https://www.youtube.com/channel/UCNJK6_DZvcMqNSzQdEkzvzA

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