https://docs.google.com/document/d/1qcGO1fLM0XrOQpICRWmllP4iKKaZQ6ZlZ78wUdhIYfg/edit?ts=5f4aa978#
- Mainly using R
- This workshop will be focused on Decision Trees and Random Forest
- Option 1: Local R & R Studio (R version 4.0.2 or later) https://cran.r-project.org/ https://rstudio.com/products/rstudio/download/
- Option 2: R Studio cloud https://rstudio.cloud/
- Option 3: Databricks Community edition (free sign-up) https://community.cloud.databricks.com/
- R Packages and datasets:
- R Packages: install.packages(c("mlbench","rpart.plot", “caret”, “rpart”, “e1071”, “randomForest”, “tidyverse”))
- Datasets: iris, Car Evaluation, Sonar
- Source of UCI ML repo: https://archive.ics.uci.edu/ml/index.php
- Workshop code and slides will be available on Friday: https://github.com/haddadz/vancvouredatajam-advancedRworkshop
1- https://en.wikipedia.org/wiki/Cross-industry_standard_process_for_data_mining
2- https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/lifecycle
3- http://homepages.vub.ac.be/~tiasguns/
4- https://machinelearningmastery.com/finalize-machine-learning-models-in-r/
5- https://www.geeksforgeeks.org/introduction-machine-learning-using-python/
6- https://medium.com/machine-learning-for-humans/why-machine-learning-matters-6164faf1df12
7- http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram
8- https://towardsdatascience.com/a-modification-of-drew-conways-data-science-venn-diagram-d5ba93037e1a
9- https://www.bigbookofr.com/machine-learning.html
10- https://www.shirin-glander.de/2018/06/intro_to_ml_workshop_heidelberg/
11- https://dlab-berkeley.github.io/Machine-Learning-in-R/slides.html#6
12- https://www.blopig.com/blog/2017/04/a-very-basic-introduction-to-random-forests-using-r/
13- https://bradleyboehmke.github.io/HOML/
14- https://r4ds.had.co.nz/
15- https://en.wikipedia.org/wiki/Random_forest
16- https://machinelearningmastery.com/machine-learning-in-r-step-by-step/
17- https://lgatto.github.io/IntroMachineLearningWithR/index.html
18- http://www.sthda.com/english/articles/35-statistical-machine-learning-essentials/141-cart-model-decision-tree-essentials/#:~:text=So%2C%20it%20is%20also%20known,package%20of%20the%20same%20name.
19- https://www.r-bloggers.com/how-to-implement-random-forests-in-r/
20- https://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_iris.html
21- https://towardsai.net/p/programming/decision-trees-explained-with-a-practical-example-fe47872d3b53
22- https://bradleyboehmke.github.io/HOML/
23- https://www.bigbookofr.com/machine-learning.html
1- Datasets: Iris, Car Evaluation, PimaIndiansDiabetes2, Boston
2- UCI ML repo: https://archive.ics.uci.edu/ml/index.php
3- https://rdrr.io/cran/mlbench/man/PimaIndiansDiabetes.html
4- https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html