Introduction to unsupervised machine learning

Unsupervised learning is a machine learning technique where no guidance is available to the learning algorithm in the form of known label values in the training data. Unsupervised learning is useful in categorizing unknown data points into groups based on patterns, similarities, or differences that are inherent within the data, without any prior knowledge of the data.

In supervised learning, a model is trained on known data, and then inferences are drawn from the model using new, unseen data. On the other hand, in unsupervised learning, the model training process in itself is the end goal, where patterns hidden within the training data are discovered during the model training process. Unsupervised learning is harder compared to supervised learning since it is difficult to ascertain if the results of an unsupervised learning algorithm are meaningful without any external evaluation, especially without access to any correctly labeled data...