# Introduction to clustering

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> [!NOTE]
> **Estimated course length:** 110 min

> [!NOTE]
> **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](https://developers.google.com/machine-learning/problem-framing) or equivalent.
- [Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course), including [Working with numerical data](https://developers.google.com/machine-learning/crash-course/numerical-data) and [Datasets, generalization, and overfitting](https://developers.google.com/machine-learning/crash-course/overfitting), or equivalent.