Create machine learning models with R and tidymodels
Learn how to explore and analyze data by using R. Get an introduction to regression models, classification models, and clustering models by using tidymodels and R.
In this learning path, you'll learn
- Common data exploration and analysis tasks
- How to use R packages like ggplot2, dplyr and tidyr to turn raw data into understanding, insight and knowledge
- When to use regression models
- How to train and evaluate regression models using the tidymodels framework
- When to use classification models
- How to train and evaluate a classification model using the tidymodels framework
- When to use clustering models
- How to train and evaluate clustering models using the tidymodels framework
Prerequisites
- Knowledge of basic mathematics
- Some experience programming in R
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Modules in this learning path
In this module, you'll explore, analyze, and visualize data by using the R programming language.
Get an introduction to regression models. In machine learning, the goal of regression is to create a model that can predict a numeric, quantifiable value.
Classification is a form of machine learning in which you train a classification model to predict which category an item belongs to. In this module, you learn how to use the R programming language and tidymodels framework to train classification models.
Get an introduction to clustering models. Clustering is the process of grouping objects with similar objects.