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Title: GitHub - Sarangsh/ISLR-python: An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code · GitHub

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https://github.com/Sarangsh/ISLR-python#islr-python
'An Introduction to Statistical Learning with Applications in R'https://www.statlearning.com/
this repositoryhttps://github.com/JWarmenhoven/DBDA-python
these package versionshttp://nbviewer.jupyter.org/github/JWarmenhoven/ISLR-python/blob/master/Notebooks/Python%20module%20versions.ipynb
python-glmnethttps://github.com/civisanalytics/python-glmnet
https://github.com/Sarangsh/ISLR-python/blob/master/Notebooks/ISL%20Cover%202.jpg
Chapter 3 - Linear Regressionhttp://nbviewer.ipython.org/github/JWarmenhoven/ISL-python/blob/master/Notebooks/Chapter%203.ipynb
Chapter 4 - Classificationhttp://nbviewer.ipython.org/github/JWarmenhoven/ISL-python/blob/master/Notebooks/Chapter%204.ipynb
Chapter 5 - Resampling Methodshttp://nbviewer.ipython.org/github/JWarmenhoven/ISL-python/blob/master/Notebooks/Chapter%205.ipynb
Chapter 6 - Linear Model Selection and Regularizationhttp://nbviewer.ipython.org/github/JWarmenhoven/ISL-python/blob/master/Notebooks/Chapter%206.ipynb
Chapter 7 - Moving Beyond Linearityhttp://nbviewer.ipython.org/github/JWarmenhoven/ISL-python/blob/master/Notebooks/Chapter%207.ipynb
Chapter 8 - Tree-Based Methodshttp://nbviewer.ipython.org/github/JWarmenhoven/ISL-python/blob/master/Notebooks/Chapter%208.ipynb
Chapter 9 - Support Vector Machineshttp://nbviewer.ipython.org/github/JWarmenhoven/ISL-python/blob/master/Notebooks/Chapter%209.ipynb
Chapter 10 - Unsupervised Learninghttp://nbviewer.ipython.org/github/JWarmenhoven/ISL-python/blob/master/Notebooks/Chapter%2010.ipynb
Extra: Misclassification rate simulation - SVM and Logistic Regressionhttp://nbviewer.jupyter.org/github/JWarmenhoven/ISL-python/blob/master/Notebooks/Simulate.expected.misclassification.rate.ipynb
R-package ISLRhttps://cran.r-project.org/web/packages/ISLR/index.html
course cataloguehttps://www.edx.org/school/stanfordonline
https://github.com/Sarangsh/ISLR-python#references
https://www.statlearning.com/https://www.statlearning.com/
http://statweb.stanford.edu/~tibs/ElemStatLearn/http://statweb.stanford.edu/~tibs/ElemStatLearn/
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