Machine Learning has many categories, which are classified based on the amount of human guidance necessary for labelling the input training data. These categories are :Supervised Learning: The algorithm is provided a set of data for training, which has labels on some segments. As an example, some data points in a data set of financial transactions may have labels, which helps identifying the ones that are fraudulent as compared to those that are genuine. Over the course of training, the system’s algorithm will ‘learn’ a high-level method of classification, which it will use to forecast the labels for the outstanding entries in the data set.Unsupervised Learning: In this category, the input data fed to the algorithm doesn’t have labels. The algorithm is requested to detect patterns in the data by identifying groups of observations that are based on comparable underlying features. For instance, an unsupervised machine learning algorithm could be defined to look for securities that have features comparable to an illiquid security that is difficult to provide an evaluation for. If it finds a similar looking cluster for the illiquid security, evaluations of other securities in the cluster can be used to help estimate the value of the illiquid security.Reinforcement Learning: This technique in somewhat part way between supervised and unsupervised learning. In this category, the algorithm is given an unlabelled set of data, and it decides an action for each data point, and receives comment or review (possibly by a human) that helps the algorithm learn. For example, reinforcement learning is particularly useful in self-driving cars, game theory, and robotics.Deep Learning: This is a type of machine learning that employs algorithms that function in ‘levels’ influenced by the design and purpose of the human brain. Deep learning algorithms and it’s structures, called artificial neural networks, can be used for supervised, unsupervised, or reinforcement learning.