Supervised Learning

How it works: —

• This algorithm consists of a target / outcome variable (or dependent variable) which is to be predicted from a given set of predictors (independent variables).
• Using these set of variables, we generate a function that map inputs to desired outputs.
• The training process continues until the model achieves a desired level of accuracy on the training data.
• Examples of Supervised Learning: Regression, Decision Tree, Random Forest, KNN, Logistic Regression etc.

Unsupervised Learning

How it works: —

• In this algorithm, we do not have any target or outcome variable to predict / estimate.
• It is used for clustering population in different groups, which is widely used for segmenting customers in different groups for specific intervention.
• Examples of Unsupervised Learning: Apriori algorithm, K-means.

Reinforcement Learning

How it works: —

• Using this algorithm, the machine is trained to make specific decisions.
• It works this way: the machine is exposed to an environment where it trains itself continually using trial and error.
• This machine learns from past experience and tries to capture the best possible knowledge to make accurate business decisions.
• Example of Reinforcement Learning: Markov Decision Process