The goal of unsupervised learning is to let the machine learn without any assistance or prompts. It should also learn to adjust the results and groupings and explore more suitable outcomes. It is allowing the machine to understand the data and process it to how it sees fit. According to its similarities, the machine groups the entities together, finding hidden structures and patterns from the unlabelled data. The data collected and received often don’t come with labels, so using unsupervised learning saves the data scientist from labelling everything, which can be time-consuming and often tedious. Unsupervised learning algorithms also allow obtaining solutions for more complex processing tasks. Having no label means that complicated relationships and clusters of data can be mapped. Using the unsupervised learning method, generative models can be created.