As it's implied in its name, Machine Learning (ML) is the science of building machines (algorithms) that can learn from data. In other words, this class of algorithms generates certain outcomes (predictions) based on the relations they infer from the training data—not from the hardcoded, predetermined rules. Usually, ML is described as having two main branches—supervised and unsupervised ML.
Unsupervised models attempt to find structure in the data itself, without any given supervision or target to focus on. The usual task is to find clusters of similar records (for example, users) to understand the underlying latent logic (for example, using target audiences and the corresponding use cases for the service).
Supervised learning is all about training the model by feeding it pairs of independent features and the correct values of...
