Unsupervised learning finds structure in unlabeled data: clustering similar items, detecting anomalies, reducing dimensionality, learning representations. No co
Finding structure in unlabeled data: clustering similar items, detecting anomalies, reducing dimensionality, or learning representations. No correct answers are provided; the algorithm discovers patterns on its own.
Its modern triumph is self-supervised pre-training, a close cousin, where models learn world structure from raw text and images at scale. In everyday business practice, clustering and anomaly detection remain its daily faces.