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index.qmd
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---
title: "Machine learning with {tidymodels}"
---
## Welcome
Summary: This workshop will provide a gentle introduction to machine learning with R using the modern suite of predictive modeling packages called **tidymodels**.
We will build, evaluate, compare, and tune predictive models. Along the way, we’ll learn about key concepts in machine learning including overfitting, the holdout method, the bias-variance trade-off, ensembling, cross-validation, and feature engineering.
Learners will gain knowledge about good predictive modeling practices, as well as hands-on experience using **tidymodels** packages like **parsnip**, **rsample**, **recipes**, **yardstick**, and **tune**.
Pre-requisites: Some introductory experience with R.
## Installation
Please join the workshop with a computer that has the following installed (all available for free):
A recent version of R, available at https://cran.r-project.org/
A recent version of RStudio Desktop (RStudio Desktop Open Source License, at least v2022.02), available at https://www.rstudio.com/download
The following R packages, which you can install from the R console:
```{r, eval=FALSE}
install.packages(c("embed", "forcats","remotes",
"tidymodels", "glmnet"))
remotes::install_github("emilhvitfeldt/elevators")
```
## Slides
+ [1: Introduction](slides/1-introduction.html)
+ [2: Models](slides/2-models.html)
+ [3: Features](slides/3-features.html)
+ [4: Resampling](slides/4-resampling.html)
+ [5: Tuning](slides/5-tuning.html)
## Labs
Link to lab for local download <a href="static/labs.Rmd" download>here</a>
## Links
+ Link to tidymodels main website <https://www.tidymodels.org/>
+ Link to "Tidy Modeling with R" book: <https://www.tmwr.org/>