Unsupervised learning trains AI on data without labels, letting the model find patterns, clusters, and relationships on its own. Rather than learning from correct answers, the model organizes data based on inherent structure it discovers.
It is useful for discovering hidden patterns in large datasets, grouping similar items without predefined categories, and identifying anomalies that differ from the rest of the data. When labeled data is unavailable or when you want to discover structure you did not know to look for, unsupervised learning is the approach.
