Decision Tree Course
Master decision trees from core theory to production deployment in one comprehensive course. You'll learn how to build, tune, evaluate, and explain tree-based models for both classification and regression tasks. From Gini impurity to gradient boosting, every concept is grounded in practical, code-ready skills.
4 to 360 hours of flexible workload
valid certificate in your country
What Will I Learn?
This course covers the complete decision tree workflow, starting with tree anatomy and splitting criteria and moving through data preparation, pruning, and performance evaluation. You will implement classification and regression trees using scikit-learn and apply ensemble methods including random forests and gradient boosting. You will learn to interpret model outputs, extract human-readable rules, and communicate findings to non-technical stakeholders. The course also addresses fairness, bias detection, and explainability tools such as SHAP and LIME. By the end, you will be able to deploy monitored, production-ready tree-based models.
Elevify Differentials
Develop Skills
- Tree Construction and Splitting: implement Gini impurity, entropy, and variance reduction from scratch.
- Overfitting Control: apply pruning strategies and hyperparameters to build generalizable tree models.
- Ensemble Model Tuning: build and optimize random forests and gradient boosting pipelines for production.
- Model Deployment and Monitoring: serialize, serve, and track decision tree models in live environments.
Suggested Summary
Before starting, you can change the chapters and workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.What our students say
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