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Course

Modeling with tidymodels in R

Intermediate

4.7+

Updated 05/2025

Learn to streamline your machine learning workflows with tidymodels.

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RMachine Learning4 hours16 videos59 Exercises4,950 XP9,695Statement of Accomplishment

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Course Description

Tidymodels is a powerful suite of R packages designed to streamline machine learning workflows. Learn to split datasets for cross-validation, preprocess data with tidymodels' recipe package, and fine-tune machine learning algorithms. You'll learn key concepts such as defining model objects and creating modeling workflows. Then, you'll apply your skills to predict home prices and classify employees by their risk of leaving a company.

Prerequisites

Modeling with Data in the Tidyverse

1

Machine Learning with tidymodels

The tidymodels ecosystem

Tidymodels packages

Creating training and test datasets

Distribution of outcome variable values

Linear regression with tidymodels

Fitting a linear regression model

Exploring estimated model parameters

Predicting home selling prices

Evaluating model performance

Model performance metrics

R squared plot

Complete model fitting process with last_fit()

2

Classification Models

Classification models

Data resampling

Fitting a logistic regression model

Combining test dataset results

Assessing model fit

Calculating metrics from the confusion matrix

Evaluating performance with yardstick

Creating custom metric sets

Visualizing model performance

Plotting the confusion matrix

ROC curves and area under the ROC curve

Automating the modeling workflow

Streamlining the modeling process

Collecting predictions and creating custom metrics

Complete modeling workflow

3

Feature Engineering

Feature engineering

Exploring recipe objects

Creating recipe objects

Training a recipe object

Numeric predictors

Discovering correlated predictors

Removing correlated predictors with recipes

Multiple feature engineering steps

Nominal predictors

Applying step_dummy() to predictors

Ordering of step_*() functions

Complete feature engineering pipeline

Complete modeling workflow

Feature engineering process

Model training and prediction

Model performance metrics

4

Workflows and Hyperparameter Tuning

Machine learning workflows

Exploring the loans dataset

Specifying a model and recipe

Creating workflows

Estimating performance with cross validation

Measuring performance with cross validation

Cross validation with logistic regression

Comparing model performance profiles

Hyperparameter tuning

Setting model hyperparameters

Random grid search

Exploring tuning results

Selecting the best model

Finalizing a workflow

Training a finalized workflow

Congratulations!

Modeling with tidymodels in R

Course
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Included withPremium or Teams

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*4.7

from 37 reviews

76%

24%

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Most recent
Top reviews

Jie

12 days

Brent

15 days

Excellent intro to tidymodels

Liam

6 days

A nice practical primer to a powerful data modeling ecosystem

Benjamín Samuel

9 days

buen curso, estuvo interesante

Jonathan

16 days

This course provided a very good job of showing us the process for using tidymodels in R. It provided limited information on understanding the models. In other words, it is a programming class, not a modeling class.

Juan Francisco

21 days

Jie

"Excellent intro to tidymodels"

Brent

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