Optimization-based learning models form the backbone of predictive systems.

This episode explains Linear Regression for continuous prediction and Logistic Regression for classification using probability-based decision boundaries.

Key topics:

• Linear Regression: Model equation and cost minimization.

• Gradient Descent: Concept of iterative optimization.

• Logistic Regression: Sigmoid function and probability output.

• Decision Boundary: Classification using thresholds.

This episode connects mathematical intuition with practical machine learning applications.

Series: Mindforge ML

Produced by: Chatake Innoworks Pvt. Ltd.

Initiative: MindforgeAI