Unsupervised machine learning is a type of machine learning where the algorithm is trained on an unlabelled dataset.

Unlabeled dataset means that there is no specific output variable that the algorithm is trying to predict.

The goal of unsupervised learning is to discover patterns and relationships in the data.

Unsupervised learning is used when the data does not have a clear output variable.

Common unsupervised learning algorithms include clustering, principal component analysis (PCA), and anomaly detection.

The quality of an unsupervised learning algorithm is typically evaluated using metrics such as cluster cohesion and separation.

Unsupervised learning is often used in exploratory data analysis to gain insights into the data.

Unsupervised learning has applications in many fields, such as image and video analysis, bioinformatics, and anomaly detection.