COURSE OUTLINE

Day 1: The Modern R Modeling Framework

Focus: Introduction to Tidymodels and data preparation.

• R for ML in 2026: Why R remains the gold standard for statistical learning and research.
• Data Splitting with rsample: Creating training/testing sets and handling class imbalance with stratification. 
• Pre-processing with recipes: Building a blueprint for data cleaning (imputation, normalization, and dummy variables).
• Hands-on: Preparing a "dirty" marketing dataset for a predictive classification task.

Day 2: Linear & Logistic Modeling

Focus: Statistical baselines and regression. 

• Linear Regression: Modeling continuous outcomes and checking for heteroscedasticity. 
• Logistic Regression: Binary classification and interpreting odds ratios.
• Penalized Models: Using Lasso and Ridge (glmnet) to handle high-dimensional data.
• Model Workflows: Bundling pre-processing and modeling into a single, clean workflow object.
• Hands-on: Predicting insurance costs using multiple regression and interaction terms.

Day 3: Tree-Based Models & Ensembles

Focus: Moving to non-linear, high-performance algorithms.

• Decision Trees: Visualizing logic splits and managing tree depth.
• Random Forests: Using ranger to build robust ensembles that reduce variance.
• Boosted Trees (XGBoost): Sequential learning for top-tier predictive accuracy.
• Hands-on: Building a "Credit Default" predictor that identifies high-risk applications.

Day 4: Resampling & Hyperparameter Tuning

Focus: Finding the "Best" version of your model. 

• Cross-Validation: Using K-Fold and Bootstrapping to ensure model stability.
• Grid Search with tune: Defining parameter grids (e.g., number of trees, learning rate).
• Racing Methods: Using finetune to efficiently discard underperforming models early.
• Hands-on: Tuning a Gradient Boosting model to maximize its AUC score.

Day 5: Model Evaluation, Interpretation & Deployment

Focus: Communicating results and moving to production.

• Performance Evaluation: Deep dive into yardstick for multi-class classification and regression metrics.
• Model Agnostic Explanations: Using VIP (Variable Importance Plots) to explain why the model makes specific decisions.
• Deployment with plumber: Turning your R model into a REST API for integration with other apps.
• Final Project: Building an end-to-end "Customer Churn" analysis from raw data to an interpreted API endpoint.