Supervised Learning

Supervised learning can be explained as follows: use labeled training data to learn the mapping function from the input variables (X) to the output variable (Y).

Y = f (X)

Supervised learning problems can be of two types:

a. Classification: To predict the outcome of a given sample where the output variable is in the form of categories. Examples include labels such as male and female, sick and healthy.

b. Regression: To predict the outcome of a given sample where the output variable is in the form of real values. Examples include real-valued labels denoting the amount of rainfall, the height of a person.

Learning algorithms

Linear Regression

Logistic Regression

CART

Naive Bayes

KNN (K-nearest neighbours)

Support Vector Machines

Decision Tree

Unsupervised Learning

Unsupervised learning problems possess only the input variables (X) but no corresponding output variables. It uses unlabeled training data to model the underlying structure of the data.

Learning algorithms

K-Means is an iterative algorithm that groups similar data into clusters.It calculates the centroids of k clusters and assigns a data point to that cluster having least distance between its centroid and the data point.