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Modeling with tidymodels in R

Intermediate

4.7+

Updated 12/2024

Learn to streamline your machine learning workflows with tidymodels.

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RMachine Learning4 hours16 videos59 exercises4,950 XP9,156Statement 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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*4.7

from 25 reviews

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Most recent
Top reviews

Andreia B.

6 months

Fantastic course!

Errin S.

8 months

Overall great lesson, with thorough walkthrough of an entire workflow. Very helpful. Few glitches when submitting answers; Usually upon running the last chunk of code it wouldn’t accept it. Had to reload the page then the same answer was accepted.

Dan R.

10 months

Well designed. However could there be more on Tidy Models?

Carlos P.

11 months

Very simple but really good.

Anders W.

12 months

Good balance between videos and Interactive exercisers.

"Fantastic course!"

Andreia B.

"Overall great lesson, with thorough walkthrough of an entire workflow. Very helpful. Few glitches when submitting answers; Usually upon running the last chunk of code it wouldn’t accept it. Had to reload the page then the same answer was accepted."

Errin S.

"Well designed. However could there be more on Tidy Models?"

Dan R.

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By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.