12.1 Unsupervised Learning

Unsupervised Learning

Works with unlabeled data, letting models discover patterns + insights without specific guidance.

Clustering

Unsupervised method, groups similar data points in large datasets without predicting specific outcomes

Dimensionality Reduction

Trims down the inputs to a manageable size while keeping the data intact

Association Rule Mining

Digs into large amounts of data + discovers interesting relations between attributes.

Anomaly Detection

Identifies unusual data points, hinting at issues by pinpointing potential outliers (Ex. such as equipment faults, human errors, or security breaches)

Density Estimation

Estimating how many data points could be at a specific location in the data