Course
Modeling with tidymodels in R
IntermediateSkill Level
Updated 07/2022RMachine Learning4 hr16 videos59 Exercises4,950 XP10,700Statement of Accomplishment
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Prerequisites
Modeling with Data in the Tidyverse1
Machine Learning with tidymodels
In this chapter, you’ll explore the rich ecosystem of R packages that power tidymodels and learn how they can streamline your machine learning workflows. You’ll then put your tidymodels skills to the test by predicting house sale prices in Seattle, Washington.
2
Classification Models
Learn how to predict categorical outcomes by training classification models. Using the skills you’ve gained so far, you’ll predict the likelihood of customers canceling their service with a telecommunications company.
3
Feature Engineering
Find out how to bake feature engineering pipelines with the recipes package. You’ll prepare numeric and categorical data to help machine learning algorithms optimize your predictions.
4
Workflows and Hyperparameter Tuning
Now it’s time to streamline the modeling process using workflows and fine-tune models with cross-validation and hyperparameter tuning. You’ll learn how to tune a decision tree classification model to predict whether a bank's customers are likely to default on their loan.
Modeling with tidymodels in R
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FAQs
What machine learning tasks does Modeling with tidymodels in R cover?
The course covers both regression and classification. You will predict house sale prices in Seattle and classify customer churn for a telecommunications company and loan defaults for a bank.
Which tidymodels packages will I use in this course?
You will work with the core tidymodels suite, including the recipes package for feature engineering and tools for defining model objects, workflows, cross-validation, and hyperparameter tuning.
Do I need prior machine learning experience before taking this course?
No machine learning experience is required, but you should know dplyr and the tidyverse. The course is beginner-level for machine learning and introduces concepts step by step.
What is feature engineering and how is it taught here?
Feature engineering transforms raw data to improve model performance. You will learn to build preprocessing pipelines with the recipes package, handling both numeric and categorical variables.
Will I learn how to tune model hyperparameters?
Yes. The final chapter teaches you how to use cross-validation and hyperparameter tuning within tidymodels workflows, applied to a decision tree classification model.
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