Unsupervised Machine Learning

Unsupervised Machine Learning refers to a type of machine learning where the model is trained on data without labeled outputs. The algorithm tries to identify hidden patterns or structures in the data, such as clustering similar data points or reducing the dimensionality of the data. Common techniques include clustering, anomaly detection, and principal component analysis (PCA). Unsupervised learning is used in applications such as customer segmentation, anomaly detection, and data compression.