Introduction to clustering

Estimated course length: 110 min

Objectives:

• Describe clustering use cases in machine learning applications.
• Choose the appropriate similarity measure for an analysis.
• Cluster data with the k-means algorithm.
• Evaluate the quality of clustering results.
• Reduce dimensionality in clustering analysis with an autoencoder.

Prerequisites

This course assumes you have the following knowledge:

• Introduction to Machine Learning Problem Framing or equivalent.
• Machine Learning Crash Course, including Working with numerical data and Datasets, generalization, and overfitting, or equivalent.