How it works: In unsupervised learning, the network learns certain patterns or behaviors in the data without seeing an example, and only by finding similarities between data points. This is specifically helpful when one needs to find similar groups in the data. Since there is no labeled data, these algorithms are helpful when the human expert does not necessarily know what to look for in the data. This family of  ML algorithms are typically used in pattern recognition and clustering. Since there is no target outcome based on which the algorithm can model relationships, these algorithms use techniques to mine the data for rules, patterns, and structures, and group data points based on similar characteristics.