Unsupervised learning

Unsupervised learning is a type of machine learning where the algorithm is trained on unlabeled data. It aims to identify patterns and structures within the data without predefined labels, utilizing techniques such as clustering, dimensionality reduction, and anomaly detection. This approach is particularly beneficial for exploratory data analysis and is often used when labeled data is not available.

Unsupervised learning was discovered on May 19th 2020 and it currently has a search volume of 33.1K with a growth of +33%.