Machine Learning algorithms can be primarily classified depending on the presence/absence of target variables.
Supervised learning : [Target is present]
* The machine learns using labelled data. The model is trained on an existing data set before it starts making decisions with the new data.
* The target variable is continuous: Linear Regression, polynomial Regression, quadratic Regression.
* The target variable is categorical: Logistic regression, Naive Bayes, KNN, SVM, Decision Tree, Gradient Boosting, ADA boosting, Bagging, Random forest etc.
Unsupervised learning : [Target is absent]
* The machine is trained on unlabelled data and without any proper guidance. It automatically infers patterns and relationships in the data by creating clusters. The model * learns through observations and deduced structures in the data.
* Principal component Analysis, Factor analysis, Singular Value Decomposition etc.
Reinforcement Learning :
* The model learns through a trial and error method. This kind of learning involves an agent that will interact with the environment to create actions and then discover errors or rewards of that action.
