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Modeling with tidymodels in R

Learn to streamline your machine learning workflows with tidymodels.

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4 Hours16 Videos59 Exercises5,131 Learners4950 XPMachine Learning Fundamentals TrackMachine Learning Scientist TrackSupervised Machine Learning Track

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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

    Free

    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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    The tidymodels ecosystem
    50 xp
    Tidymodels packages
    100 xp
    Creating training and test datasets
    100 xp
    Distribution of outcome variable values
    100 xp
    Linear regression with tidymodels
    50 xp
    Fitting a linear regression model
    100 xp
    Exploring estimated model parameters
    50 xp
    Predicting home selling prices
    100 xp
    Evaluating model performance
    50 xp
    Model performance metrics
    100 xp
    R squared plot
    100 xp
    Complete model fitting process with last_fit()
    100 xp

In the following tracks

Machine Learning FundamentalsMachine Learning ScientistSupervised Machine Learning

Collaborators

maggiematsui
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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