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Supervised Machine Learning in R

Generate, explore, evaluate, and tune the parameters of different supervised machine learning models.
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RMachine Learning25 hours2,475

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Track Description

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.

Prerequisites

There are no prerequisites for this track

  • Course

    1

    Machine Learning in the Tidyverse

    Leverage tidyr and purrr packages in the tidyverse to generate, explore, and evaluate machine learning models.

  • Project

    bonus

    Assessing the Effectiveness of Medical Treatments

    Use logistic regression to determine which treatment procedure is more effective for kidney stone removal.

Supervised Machine Learning in R
6 Courses
Track
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