📈 Machine Learning from the perspective of a Statistician using R
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01-prerequisites.Rmd
02-probability.Rmd
03-r-rstudio-rmarkdown.Rmd
04-model-basics.Rmd
05-regression.Rmd
06-linear.Rmd
07-knn-reg.Rmd
08-tradeoff.Rmd
09-classification.Rmd
10-logistic.Rmd
11-generative.Rmd
12-knn-class.Rmd
13-unsupervised.Rmd
14-pca.Rmd
15-k-means.Rmd
16-mixture.Rmd
17-hierarchical.Rmd
18-practice.Rmd
19-supervised-recap.Rmd
20-resampling.Rmd
21-caret.Rmd
22-subset.Rmd
23-modern.Rmd
24-regularization.Rmd
25-elnet.Rmd
26-tree.Rmd
27-ensemble.Rmd
28-ann.Rmd
29-appendix.Rmd
30-nonlin.Rmd
31-rda.Rmd
32-svm.Rmd
DESCRIPTION
README.md
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_output.yml
book.bib
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index.Rmd
preamble.tex
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README.md

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r4sl

This text is currently written as a supplement to ISL for use in STAT 430, Basics of Statistical Learning at The University of Illinois at Urbana-Champaign. Some additional details and additional topics are included, but the main focus in on providing more thorough R examples.