Learn about Ensemble Methods and their implementation from scratch. This course covers the understanding and implementation of multiple ensemble methods such as Bagging, Random Forest, AdaBoost, and Gradient Boosting Machines like XGBoost without relying on high-level libraries.
Overview
Syllabus
- Lesson 1: Implementing Bagging with Decision Trees in Python
- Lesson 2: Deep Dive into Random Forest: From Concepts to Real-World Application
- Lesson 3: Demystifying AdaBoost: A Practical Guide to Strengthening Predictive Models
- Lesson 4: Enhancing Machine Learning Predictions with Stacking Ensemble Techniques
