After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
Course Description
Tree-based machine learning models can reveal complex non-linear relationships in data and often dominate machine learning competitions. In this course, you'll use the tidymodels package to explore and build different tree-based models—from simple decision trees to complex random forests. You’ll also learn to use boosted trees, a powerful machine learning technique that uses ensemble learning to build high-performing predictive models. Along the way, you'll work with health and credit risk data to predict the incidence of diabetes and customer churn.
Prerequisites
Curriculum
Course outline
1
Classification Trees
Ready to build a real machine learning pipeline? Complete step-by-step exercises to learn how to create decision trees, split your data, and predict which patients are most likely to suffer from diabetes. Last but not least, you’ll build performance measures to assess your models and judge your predictions.
- Welcome to the course!50 XP
- Why tree-based methods?100 XP
- Specify that tree100 XP
- Train that model100 XP
- How to grow your tree50 XP
- Train/test split100 XP
- Avoiding class imbalances100 XP
- From zero to hero100 XP
- Predict and evaluate50 XP
- Make predictions100 XP
- Crack the matrix100 XP
- Are you predicting correctly?100 XP
2
Regression Trees and Cross-Validation
Ready for some candy? Use a chocolate rating dataset to build regression trees and assess their performance using suitable error measures. You’ll overcome statistical insecurities of single train/test splits by applying sweet techniques like cross-validation and then dive even deeper by mastering the bias-variance tradeoff.
3
Hyperparameters and Ensemble Models
Time to get serious with tuning your hyperparameters and interpreting receiver operating characteristic (ROC) curves. In this chapter, you’ll leverage the wisdom of the crowd with ensemble models like bagging or random forests and build ensembles that forecast which credit card customers are most likely to churn.
4
Boosted Trees
Ready for the high society of tree-based models? Apply gradient boosting to create powerful ensembles that perform better than anything that you have seen or built. Learn about their fine-tuning and how to compare different models to pick a winner for production.
R
Machine Learning with Tree-Based Models in R
Course
Complete
