Introduction to clustering
- 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, includingWorking with numerical dataandDatasets, generalization, and overfitting,or equivalent.
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Last updated 2025-08-25 UTC.