Python: Implement & Evaluate Random Forests for ML

This hands-on course equips learners with the skills to implement, analyze, and evaluate the Random Forest algorithm using Python. Designed around a real-world classification problem using the SONAR dataset, the course guides learners through the entire pipeline—from data loading and preprocessing to constructing decision trees and assembling Random Forest models. Through code-driven lessons and guided quizzes, learners will apply supervised learning techniques, calculate model performance using cross-validation, and assess decision boundaries using impurity measures like the Gini index. Part…