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Dernière mise à jour le 2025/07/27 (UTC).
[null,null,["Dernière mise à jour le 2025/07/27 (UTC)."],[[["\u003cp\u003eThis course explores clustering use cases, appropriate similarity measures, and the k-means algorithm for data clustering.\u003c/p\u003e\n"],["\u003cp\u003eLearners will gain skills in evaluating clustering results and applying dimensionality reduction techniques like autoencoders.\u003c/p\u003e\n"],["\u003cp\u003eBasic knowledge of machine learning problem framing and core concepts like numerical data handling and overfitting is required.\u003c/p\u003e\n"]]],[],null,["# Introduction to clustering\n\n\u003cbr /\u003e\n\n| **Estimated course length:** 110 min\n| **Objectives:**\n|\n| - Describe clustering use cases in machine learning applications.\n| - Choose the appropriate similarity measure for an analysis.\n| - Cluster data with the k-means algorithm.\n| - Evaluate the quality of clustering results.\n| - Reduce dimensionality in clustering analysis with an autoencoder.\n\nPrerequisites\n-------------\n\nThis course assumes you have the following knowledge:\n\n- [Introduction to Machine Learning Problem Framing](/machine-learning/problem-framing) or equivalent.\n- [Machine Learning Crash Course](/machine-learning/crash-course), including [Working with numerical data](/machine-learning/crash-course/numerical-data) and [Datasets, generalization, and overfitting](https://developers.google.com/machine-learning/crash-course/overfitting), or equivalent."]]
