Unsupervised Learning
A broad machine learning paradigm where models attempt to find hidden structures, patterns, or clusters in data entirely without human-provided labels.
Think of It Like This
Like dropping a child into a room full of random mixed Lego blocks and watching them naturally group them by color and size.
Because labeling data is expensive, unsupervised learning is highly valuable. Common techniques include K-Means clustering for customer segmentation, Principal Component Analysis (PCA) for dimensionality reduction, and Autoencoders for anomaly detection. The model's sole objective is to model the underlying probability density of the data.