the-elements-of-statistical-learning VS ISLR

Compare the-elements-of-statistical-learning vs ISLR and see what are their differences.

the-elements-of-statistical-learning

My notes and codes (jupyter notebooks) for the "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani and Jerome Friedman (by maitbayev)

ISLR

Introduction to Statistical Learning (by melling)
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the-elements-of-statistical-learning ISLR
1 4
382 54
- -
1.9 0.0
over 1 year ago over 2 years ago
Jupyter Notebook R
MIT License -
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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the-elements-of-statistical-learning

Posts with mentions or reviews of the-elements-of-statistical-learning. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2020-12-31.

ISLR

Posts with mentions or reviews of ISLR. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-07-09.

What are some alternatives?

When comparing the-elements-of-statistical-learning and ISLR you can also consider the following projects:

ISL-python - Porting the R code in ISL to python. Labs and exercises

homemade-machine-learning - 🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained

Neptune.jl - Simple (Pluto-based) non-reactive notebooks for Julia

rmarkdown - Dynamic Documents for R

Pluto.jl - 🎈 Simple reactive notebooks for Julia

here_here - I love the here package. Here's why.

jupytext - Jupyter Notebooks as Markdown Documents, Julia, Python or R scripts

Sharing_ISL_python - An Introduction to Statistical Learning with Applications in PYTHON

codebraid - Live code in Pandoc Markdown

islr-for-gis - A repository for the content that is used and generated while completing independent study on using statistical learning methods in GIS.

StatsWithJuliaBook

ISLR.jl - JuliaLang version of "An Introduction to Statistical Learning: With Applications in R"