Class 13: Supervised Machine Learning Deep Dive

Summary: Comprehensive exploration of supervised learning algorithms including regression and classification techniques.

Learning Objectives:

• Implement regression algorithms for prediction tasks
• Apply classification algorithms to categorical problems
• Evaluate model performance using appropriate metrics

Key Topics:

• Linear and logistic regression implementation
• Decision trees and random forests
• Support vector machines (SVM) principles
• Model evaluation: Accuracy, precision, recall, F1-score

Activities:

• Build predictive models on real datasets
• Comparative algorithm performance analysis

Hyperparameter tuning workshop