Udemy – R tidymodels part 4: Advanced classification techniques

You’ve mastered regression, explored non-linear models, and learned the foundations of binary classification. Now it’s time to tackle complex classification problems with advanced algorithms, multiclass strategies, and scalable workflows. This course is designed for learners who want to move beyond simple two-class outcomes and master high-performance tree-based models and multi-category classification using R’s modern machine learning framework.

What You’ll Learn

In this course, you’ll expand your classification toolkit to handle sophisticated, real-world predictive challenges using tidymodels:

• Build and interpret Decision Trees specifically optimized for classification tasks.
• Harness Random Forests and Gradient Boosted Trees to capture complex non-linear interactions and boost accuracy.
• Extend binary models to Multiclass Classification to predict outcomes with three or more categories.
• Implement Multiclass Logistic Regression, K-Nearest Neighbors (KNN), and tree ensembles across multi-category data.
• Master multiclass evaluation metrics, including macro/micro averages, multiclass ROC curves, and multi-label AUC.
• Apply tidy workflows for model specification, cross-validation, hyperparameter tuning, and performance evaluation across complex model architectures.
• Complete hands-on exercises and real-world assignments (e.g., predicting heart disease risk and bank marketing outcomes).
• Execute a comprehensive Capstone Final Project, walking through the end-to-end ML lifecycle from EDA to final model testing.