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https://github.com/juliasilge/supervised-ml-case-studies-course

Supervised machine learning case studies in R! 💫 A free interactive tidymodels course
https://github.com/juliasilge/supervised-ml-case-studies-course

machine-learning online-course rstats supervised-machine-learning

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Supervised machine learning case studies in R! 💫 A free interactive tidymodels course

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README

        



# Welcome to the course repo for **[Supervised Machine Learning: Case Studies in R](https://supervised-ml-course.netlify.com/)**! 🎉

You can access [this course for free online](https://supervised-ml-course.netlify.com/).

This course approaches supervised machine learning using:

- the [tidyverse](https://www.tidyverse.org/)
- the [tidymodels](https://www.tidymodels.org/) ecosystem

The interactive course site is built on the amazing framework created by [Ines Montani](https://ines.io/), originally built for her [spaCy course](https://course.spacy.io). The front-end is powered by
[Gatsby](http://gatsbyjs.org/) and [Reveal.js](https://revealjs.com) and the
back-end code execution uses [Binder](https://mybinder.org). [Florencia D'Andrea](https://florencia.netlify.com/) helped port the course materials and made the fun logo.

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/juliasilge/supervised-ML-case-studies-course/binder)
![Netlify Status](https://api.netlify.com/api/v1/badges/3ba21376-9a18-4cf0-960e-2c65e6bc2bbd/deploy-status)

To learn more about building a course on this framework, see Ines's starter repos for making courses in [Python](https://github.com/ines/course-starter-python) and [R](https://github.com/ines/course-starter-r), and her explanation of how the framework works at [the original course repo](https://github.com/ines/spacy-course#-faq). The original version of this course based on the R package caret [is available here](https://caret-ml-course.netlify.app/).

Please note that this project is released with a [Contributor Code of Conduct](CODE_OF_CONDUCT.md). By contributing to this project, you agree to abide by its terms.

The course material in this course is licensed [CC-BY-SA](https://creativecommons.org/licenses/by-sa/4.0/), meaning you are free to use it, change it, and remix it as long as you give appropriate credit and distribute any new materials under the same license. The _code_ is [MIT](https://opensource.org/licenses/MIT)-licensed.