Unsupervised Learning
Finding structure in unlabeled data, such as grouping similar items together.
Unsupervised learning trains models on data with no human-provided labels, letting the algorithm discover hidden patterns on its own. Common techniques include clustering (grouping similar points, as in customer segmentation), anomaly detection (flagging outliers), and dimensionality reduction. Amazon SageMaker offers unsupervised built-in algorithms such as K-Means and Random Cut Forest. The key exam distinction: unsupervised learning has no ground-truth target variable to optimize against, unlike supervised learning where a labeled outcome drives training. That absence makes evaluation harder, since there is no straightforward accuracy metric.
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