A type of machine learning in which the algorithm is not provided with any pre-assigned labels or scores for the training data. As a result, unsupervised learning algorithms must first self-discover any naturally occurring patterns in the training data set. Unsupervised learning is preferable as it is easy to get unlabeled data in comparison to labeled data, but the result might be less accurate as input data is not labeled, and algorithms do not know the exact output in advance. The global Machine Learning Market is anticipated to value $96.7 billion until 2025, expected to register a CAGR of 43.8% over 2019-2025.
