Machine Learning in the Tidyverse: R Workflows

Machine Learning in the Tidyverse: R Workflows

Korshub's take

The Korshub Take

This intermediate R course integrates machine learning workflows seamlessly with Tidyverse packages. By leveraging tidy principles, you will learn to manage, tune, and evaluate multiple predictive models in a clean and reproducible framework.

Who It's For

R users and data analysts who want to apply Tidyverse packages like tidyr, purrr, and broom to construct organized machine learning pipelines.

Key Takeaways

List Column Workflow (LCW): Manage and organize multiple machine learning models inside a single tidy dataframe.
Model Tidying with broom: Cleanly extract and interpret complex model outputs and evaluation metrics using the broom package.
Tidy Pipelines: Use tidyr and purrr to execute hyperparameter tuning, regression, and classification tasks across datasets like Gapminder.

About this course

Welcome to the tidyverse! In this course, you will continue on your journey to learn the tidyverse and apply your knowledge to machine learning concepts.

This course is ideal if you’re looking to integrate R's Tidyverse tools into your machine learning workflows.

Evaluating machine learning models

Throughout this course, you will focus on leveraging the tidyverse tools in R to build, explore, and evaluate machine learning models efficiently.

The course begins by introducing the List Column Workflow (LCW), a method for managing multiple models within a single dataframe. It also covers using the broom package to tidy up and explore model outputs, making the complex results more interpretable.

Utilizing tidyr and purrr

Work through practical exercises including building and evaluating regression along with classification models. Explore techniques for tuning hyperparameters to optimize model performance.

You will use packages like tidyr and purrr to handle complex data manipulations and model evaluations, ensuring a tidy and systematic approach to machine learning.

Gain real-world application

Explore real-world examples through multiple case studies, such as using the gapminder dataset to predict life expectancy with linear models.

By the end of the course, you will have a strong foundation in applying Tidyverse principles to machine learning, enabling them to build, tune, and evaluate models efficiently in a tidy and reproducible manner.

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