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DataCamp

Supervised Machine Learning in R

via DataCamp

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Overview

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.

Syllabus

  • Machine Learning in the Tidyverse
    • Leverage tidyr and purrr packages in the tidyverse to generate, explore, and evaluate machine learning models.
  • Intermediate Regression in R
    • Learn to perform linear and logistic regression with multiple explanatory variables.
  • Assessing the Effectiveness of Medical Treatments
  • Modeling with tidymodels in R
    • Learn to streamline your machine learning workflows with tidymodels.
  • 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.
  • Support Vector Machines in R
    • This course will introduce the support vector machine (SVM) using an intuitive, visual approach.
  • Hyperparameter Tuning in R
    • Learn how to tune your model's hyperparameters to get the best predictive results.

Taught by

Shirin Elsinghorst (formerly Glander), Kailash Awati, Dmitriy Gorenshteyn, Richie Cotton, David Svancer, and Sandro Raabe

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