Lilly Tech Systems
R for Machine Learning
Build and evaluate machine learning models in R — from regression and classification to clustering, using tidymodels, caret, and mlr3 frameworks.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
Overview of the ML ecosystem in R, framework comparison, and setting up your environment.
2. tidymodels Framework
The modern ML framework: recipes, parsnip, workflows, tune, and yardstick.
3. Regression
Linear regression, ridge/lasso, decision trees, random forests, and XGBoost for regression.
4. Classification
Logistic regression, SVM, KNN, random forests, XGBoost, and evaluation metrics.
5. Clustering
K-Means, hierarchical clustering, DBSCAN, PCA, t-SNE, and cluster visualization.
6. Model Evaluation
Cross-validation, hyperparameter tuning, learning curves, and model comparison.
7. caret & mlr3
Alternative ML frameworks: caret's train() and mlr3's task/learner paradigm.
8. Best Practices
Reproducibility, model deployment with plumber/vetiver, Docker, and production ML.
What You'll Learn
By the end of this course, you'll be able to:
Build ML Pipelines
Create end-to-end machine learning pipelines using the tidymodels framework.
Train Multiple Models
Implement regression, classification, and clustering algorithms with real data.
Evaluate & Tune
Use cross-validation, hyperparameter tuning, and proper evaluation metrics.
Deploy Models
Serve R models as APIs using plumber and vetiver for production use.