This episode provides a step-by-step conceptual understanding of K-Means clustering — one of the most important unsupervised learning algorithms.

Key topics:

• Clustering concept: Grouping similar data points.

• Centroid: Center of a cluster.

• Algorithm steps: Initialization, assignment, and update.

• Distance calculation: Measuring similarity.

• Choosing K: Elbow method and intuition.

This episode connects theory with intuitive understanding and practical relevance.

Series: Mindforge ML

Produced by: Chatake Innoworks Pvt. Ltd.

Initiative: MindforgeAI