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Unsupervised Learning

Unsupervised learning is a type of machine learning where algorithms analyze unlabeled data to discover hidden patterns, structures, or relationships. Unlike supervised learning, there are no predefined outputs – models must interpret the data on their own. Common techniques include clustering, dimensionality reduction, and anomaly detection, which help uncover natural groupings or simplify complex datasets. This approach is widely used in areas like customer segmentation, fraud detection, and exploratory data analysis. Unsupervised learning plays a key role in developing adaptive AI systems capable of understanding raw data and generating insights without human intervention.