Type of machine learning where the model finds patterns in unlabeled data. The algorithm discovers hidden structure in the data. Main technique: clustering.
In AI-901: used when you don't have labeled data. Example: segmenting customers into groups by purchasing behavior without knowing beforehand how many groups exist. Key difference from supervised learning: there's no "correct answer" during training.
Unsupervised learning is a concept that can show up on the exam. Check the full guide to see it in context.