Unsupervised learning is a paradigm in machine learning where algorithms learn patterns exclusively from unlabeled data. "During the learning phase, an unsupervised network tries to mimic the data it's given and uses the error in its mimicked output to correct itself (i.e. correct its weights and biases). Sometimes the error is expressed as a low probability that the erroneous output occurs."[1]
Some of the most common algorithms used in unsupervised learning include clustering and anomaly detection, "these are techniques used by many companies today, in important commercial applications."[2]
[1] Unsupervised_learning, Wikipedia
[2] Andrew Ng, Stanford University & DeepLearning.AI, Machine Learning Specialization, Course 3, Week 1