Before understanding algorithms, clarity in fundamentals is essential.

This episode explores the core concepts of supervised learning including labeled datasets, regression and classification problems, and the supervised learning workflow.

Key topics:

• Input–Output Mapping: Y = f(X) intuition.

• Training vs Testing: Model learning and validation.

• Regression and Classification: Problem type distinction.

• Overfitting and Underfitting: Early introduction.

This episode builds the conceptual base for all supervised algorithms.

Series: Mindforge ML

Produced by: Chatake Innoworks Pvt. Ltd.

Initiative: MindforgeAI