Machine Learning Archive

This repository archives useful R-related books and repositories for machine learning. The recent development comes from tidyverse and tidymodels, and following cites are useful for catch-up.

• 🎁 Tidy data science workshop :https://tidy-ds.wjakethompson.com/
• 🎁 Remaster tidyverse: https://github.com/rstudio-education/remaster-the-tidyverse
• 🎁 An Antarctic Tour of the Tidyverse (R-Ladies Chicago): https://silvia.rbind.io/talk/2020-08-31-tour-of-the-tidyverse/

For latest papers, browse the state-of-the art is useful for finding the paper and code on GitHub.
https://paperswithcode.com/sota

ML Overview

Material	Link/Repository
Applied ML	https://github.com/kojimizu/rstudio-conf-2018
Hands-on ML with R	http://bit.ly/HOML_with_R
UC Business Analytics with R	http://uc-r.github.io
Data Analysis and Prediction Algorithms with R	https://rafalab.github.io/dsbook/

Applied Machine Learning

Applied ML workshop by Max Kuhn from useR/rstudio-conf Conference

• rstudio-conf-2019 repository: https://github.com/topepo/rstudio-conf-2019
• rstudio-conf-2018 repository: https://github.com/kojimizu/rstudio-conf-2018

📚 Hands on Machine Learning with R

Hands-on Machine Learning with R: An applied book covering the fundamentals of machine learning with R.
This book covers multiple models 1) gm, 2) regularized glm, 3) random forest and 4) gradient boosting methods.
From 2018 to 2019 January
📚 Book: http://bit.ly/HOML_with_R
📦 Repo: https://github.com/bradleyboehmke/hands-on-machine-learning-with-r

📚 UC Business analyrics by R

This material from the below page covers basic R techniques, descrptive analytics, and predictive analytics.
📚 Book: University of Cincinatti - http://uc-r.github.io

• Predictive analytics / Supervised learning: Linear, Naive Bayes, Reularized Regression (Ridge, LASSO), MARS, Regression Tree, Random Forest, GBM, Discriminant Analysis, SVM
• Descriptive analytics / Unsupervised learning (K-means, PCA, Hierarchical clustering), Text mining, classical methods
• Time series analysis: Exponential smoothing, MA/AR
• DL: Regression DNN, Classification DNN

--- updated until here on 11/02/2021

📚 Introduction to data science

Modeling algorithm by R. 📚 Book: https://rafalab.github.io/dsbook/
📦 Repo: https://github.com/rafalab/dsbook/tree/master/docs

ML Interpretability

Intepretable Machine Learning

📚 Book: https://christophm.github.io/interpretable-ml-book/

Limitations of Interpretable Machine Learning Methods

📚 Book: https://compstat-lmu.github.io/iml_methods_limitations/

Modeling Resources

📦 Caret

Caret package introduction by Max Kuhn (Bookdown) A package for ML modeling with pre-processing techniques 📚 Book: http://topepo.github.io/caret/
📦 Repo: https://github.com/topepo/caret

# Avaiable from CRAN
install.packages("caret")

📦 Tidymodels

Tidymodels is a multi-package containing modern tidyverse-based packages. Tidymodel usecase is prepared by the dev team: https://www.tidymodels.org/learn/

📦Learn: https://www.tidymodels.org/learn/ 📦 Repo: https://github.com/tidymodels/tidymodels

• broom: tidy, augment, glance for model interpretation support
• rsample: data resampling
• recipes: pre-processing functions (similar to caret)
• parsnip: modeling functions

Useful blog posts recipes: http://www.rebeccabarter.com/blog/2019-06-06_pre_processing/
tidymodels: https://rviews.rstudio.com/2019/06/19/a-gentle-intro-to-tidymodels/
Tutorial by RStudio: https://github.com/tidymodels/aml-training

Feature Engineering Resouces

📚 Feature Engineering and Selection (FES)

A Bookdown page prepared by Max Kuhn
📚 Book: http://www.feat.engineering/
📦 Repo: https://github.com/kojimizu/FES

📚 Feature Engineering Book

by Alice Zheng

=========================================

Natural Language Processing (NLP)

Supervised machine learning for text analysis

The latest book on 2020/07 by Julia Silge and EMIL HVITFELDT

📚 Book: https://smltar.com/language.html
📦 Repo: https://github.com/EmilHvitfeldt/smltar

=========================================

Modern R

Modern commands

📚 R for Data Science

Basic commands with tidyverse is summarised. 📚 Book: https://r4ds.had.co.nz/
The excercise solution can be found on Bookdown as well.

📚 Modern R with Tidyverse

Bookdown material is available here, covering:

Datacamp courses

• 🎥 Machine Learning in the Tidyverse.

Purrr material

RStudio

https://purrr.tidyverse.org/

Happy Purrrr

• 🎥 A tutorial video by Charotte Wickhkm. THe DataCamp courses are useful for code practice.
• 🎥 Functional progamming with purrr by DataCamp

Case study of Purrr

Lessons and Examples by Jenny Brian: 📚 Web: https://jennybc.github.io/purrr-tutorial/

• Background basics
• Core purrr lessons
• Worked examples

=========================================

Visualization

📚 Data visualition - A practical introduction

by Kieran Healy.
📚 Book: http://socviz.co/index.html.

📚 R Graphics cookbook

by Winston Chang 📚 Book: https://r-graphics.org/
Another useful website is Cookbook for R: covering other topics online: http://www.cookbook-r.com/

Geo-visualization

• Site: https://www.jasondavies.com/

ggplot tutorial

https://cedricscherer.netlify.com/2019/08/05/a-ggplot2-tutorial-for-beautiful-plotting-in-r/#toc

Data viz packages

Rayshader:

3D plot including ggplot2 viz: https://www.tylermw.com/

Uber's H3

Hexigon-shaped viz:
site: http://estrellita.hatenablog.com/entry/2019/05/01/235406

Mapdeck

Blogpost: http://estrellita.hatenablog.com/entry/2018/09/25/230000_3

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Stats by Uni Class / Workshop

Stat

Lecture by Jenny Brian:

Stanford CS229

Machine learning course by Andrew Ng from Stanford University
🎥Video and 📚Material : http://cs229.stanford.edu/syllabus.html

Statistical Rethinking

Bayes stats using brms, ggplot2 and tidyverse

• 📚 Bookdown: https://bookdown.org/ajkurz/Statistical_Rethinking_recoded/
• HP: https://xcelab.net/rm/statistical-rethinking/

Deep learning with TF and Keras in R

Workshop for Rstudio::2020 by Bradley Boemeike.
Repo: https://github.com/kojimizu/dl-keras-tf