Modeling with tidymodels in R

Learn to streamline your machine learning workflows with tidymodels.
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4 Hours16 Videos59 Exercises2,443 Learners
4950 XP

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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.

  1. 1

    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.
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  2. 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.
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  3. 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.
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  4. 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.
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Home salesLoansTelecom dataEmployee dataPurchase leads
Maggie Matsui
David Svancer Headshot

David Svancer

Data Scientist
David is a data scientist in the Washington D.C. area where he helps organizations leverage data science and machine learning to solve complex business problems and build data products. He is also an adjunct professor of Business Analytics in the Graduate School of Business at George Mason University where he teaches courses focused on applied statistics, data analysis, machine learning, and database design.
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