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Unsupervised learning algorithms accept unlabeled data and attempt to group observations into categories based on underlying similarities in input features, as shown in Figure 3-2. Principal component analysis, k-means clustering, and singular value decomposition are all examples of unsupervised machine learning algorithms. Popular use cases include recommendation engines, facial recognition systems, and customer segmentation.

FIGURE 3-2: Unsupervised machine learning breaks down unlabeled data into subgroups.