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Introduction to clustering | Machine Learning

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• 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.

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.

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2025-02-25 UTC.

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