Supervised Machine Learning in R

Actualizat 03.2026

Generate, explore, evaluate, and tune the parameters of different supervised machine learning models.

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Supervised Machine Learning in R

Supervised learning methods are central to your journey in data science. Learn how to generate, explore, and evaluate machine learning models by leveraging the tools in the Tidyverse. You'll learn about multiple and logistic regression techniques, tree-based models, and support vector machines. Finally, you'll learn how to tune your model's parameters for better performance.

Cerințe preliminare

Nu există condiții preliminare pentru această pistă

Course
1
Machine Learning in the Tidyverse
Leverage tidyr and purrr packages in the tidyverse to generate, explore, and evaluate machine learning models.
Course
2
Intermediate Regression in R
Learn to perform linear and logistic regression with multiple explanatory variables.
Project
Bonus
Assessing the Effectiveness of Medical Treatments
Use logistic regression to determine which treatment procedure is more effective for kidney stone removal.
Course
4
Modeling with tidymodels in R
Learn to streamline your machine learning workflows with tidymodels.
Course
5
Machine Learning with Tree-Based Models in R
Learn how to use tree-based models and ensembles to make classification and regression predictions with tidymodels.
Course
6
Support Vector Machines in R
This course will introduce the support vector machine (SVM) using an intuitive, visual approach.
Course
7
Hyperparameter Tuning in R
Learn how to tune your model's hyperparameters to get the best predictive results.