Create machine learning models with R and tidymodels

In this module, you'll learn:

• Common data exploration and analysis tasks.
• How to use R packages such as ggplot2, dplyr, and tidyr to turn raw data into understanding, insight, and knowledge.

In this module, you'll learn:

• When to use regression models.
• How to train and evaluate regression models by using the tidymodels framework.

In this module, you'll learn:

• When to use classification.
• How to train and evaluate a classification model by using the tidymodels framework.

• When to use clustering models
• How to train and evaluate clustering models by using the tidymodels framework

• Introduction
• Exploratory data analysis
• Exercise - Transform data by using dplyr
• Visualize your data
• Exercise - Visualize your data by using ggplot2
• Examine real-world data
• Exercise - Examine real-world data
• Knowledge check
• Challenge - Data exploration
• Summary

• Introduction
• What is regression?
• Exercise - Train and evaluate a regression model
• Discover new regression models
• Exercise - Experiment with more powerful regression models
• Improve models with hyperparameters
• Exercise - Optimize and save models
• Knowledge check
• Challenge - Regression
• Summary

• Introduction
• What is classification?
• Exercise - Train and evaluate a binary classification model
• Evaluate classification models
• Exercise - Train a classification model by using alternative metrics
• Create multiclass classification models
• Exercise - Train and evaluate multiclass classification models
• Knowledge check
• Challenge - Train a classification model to classify wine data
• Summary

• Introduction
• What is clustering?
• Exercise - Train and evaluate a clustering model
• Evaluate different types of clustering
• Exercise - Train and evaluate advanced clustering models
• Knowledge check
• Challenge - Clustering
• Summary