Class 12: Introduction to Machine Learning Concepts

Summary: Comprehensive overview of machine learning paradigms, algorithms, and applications that form the foundation for AI systems.

Learning Objectives:

• Distinguish between supervised, unsupervised, and reinforcement learning
• Understand fundamental ML algorithms and their applications
• Recognize when to apply different ML approaches

Key Topics:

• Machine learning types and learning paradigms
• Supervised learning: Regression and classification fundamentals
• Unsupervised learning: Clustering and dimensionality reduction
• Model evaluation metrics and validation techniques

Activities:

• Algorithm selection decision tree development
• ML application brainstorming session
• Conceptual model design for business problems