- Module 1: In this module, you'll explore, analyze, and visualize data by using the R programming language.
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.
- Module 2: Introduction to regression models by using R and tidymodels.
In this module, you'll learn:
- When to use regression models.
- How to train and evaluate regression models by using the tidymodels framework.
- Module 3: Learn how to train classification models by using the R programming language and 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.
- Module 4: Introduction to clustering models by using R and tidymodels.
- When to use clustering models
- How to train and evaluate clustering models by using the tidymodels framework
Create machine learning models with R and tidymodels
Overview
Syllabus
- Module 1: Module 1: Explore and analyze data with R
- 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
- Module 2: Module 2: Introduction to regression models by using R and tidymodels
- 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
- Module 3: Module 3: Introduction to classification models by using R and tidymodels
- 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
- Module 4: Module 4: Introduction to clustering models by using R and tidymodels
- 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
