ML Problem Framing and Core Concepts

Distinguish between supervised, unsupervised, and reinforcement learning with examples of when to use each.

Differentiate between classification, regression, and clustering tasks given a business problem.

Identify when to use ML vs. rule-based approaches for a given problem.

Explain the bias-variance tradeoff and its impact on model generalization.

Describe common loss functions (e.g., MSE, cross-entropy) and when each applies.