先備知識
本課程假設您具備下列知識：
- 機器學習問題界定簡介或同等課程。
- 機器學習密集課程，包括使用數值資料和資料集、泛化和過度擬合，或其他同等課程。
分群法簡介
除非另有註明，否則本頁面中的內容是採用創用 CC 姓名標示 4.0 授權，程式碼範例則為阿帕契 2.0 授權。詳情請參閱《Google Developers 網站政策》。Java 是 Oracle 和/或其關聯企業的註冊商標。
上次更新時間：2025-07-27 (世界標準時間)。
[null,null,["上次更新時間：2025-07-27 (世界標準時間)。"],[[["\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,["\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\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."]]