Course
Machine Learning in the Tidyverse
IntermediateSkill Level
Updated 12/2022
RMachine Learning5 hr15 videos52 Exercises4,300 XP16,260Statement of Accomplishment
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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.
Prerequisites
Modeling with Data in the Tidyverse1
Foundations of "tidy" Machine learning
This chapter will introduce you to the backbone of machine learning in the tidyverse, the List Column Workflow (LCW). The LCW will empower you to work with many models in one dataframe.
This chapter will also introduce you to the fundamentals of the broom package for exploring your models.
This chapter will also introduce you to the fundamentals of the broom package for exploring your models.
2
Multiple Models with broom
This chapter leverages the List Column Workflow to build and explore the attributes of 77 models. You will use the tools from the broom package to gain a multidimensional understanding of all of these models.
3
Build, Tune & Evaluate Regression Models
In this chapter you will learn how to use the List Column Workflow to build, tune and evaluate regression models. You will have the chance to work with two types of models: linear models and random forest models.
4
Build, Tune & Evaluate Classification Models
In this chapter you will shift gears to build, tune and evaluate classification models.
Machine Learning in the Tidyverse
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FAQs
What packages are covered in this course?
The following tidyverse packages are covered in this course: tidyr, purrr, broom, tidymodels, dplyr, and ggplot2.
Do I need to have prior experience in R?
Having a foundational understanding of R is important for this course. Modeling with Data in the Tidyverse is a course prerequisite.
Do I need to have R on my computer?
You do not need to have R or any of the tidyverse packages (tidyr or purrr) on your computer. All exercises are done in-browser within this course.
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