Supervised learning means that first some dataset needs to be provided and for each data in the dataset, there is a corresponding correct answer and the (training set) algorithm is making predictions based on these correct answers. It is further divided into regression problems and classification problems.

1.Regression problems: predicting a continuous output value by regression.

2.Classification problem: Predicting the output of a discrete value by classification.

Example 1:

Given data about the size of houses on the real estate market, try to predict their price. Price
as a function of size is a continuous output, so this is a regression problem.
We could turn this example into a classification problem by instead making our output about
whether the house "sells for more or less than the asking price." Here we are classifying the
houses based on price into two discrete categories.

Is a straight line fit or a quadratic function fit better

Example 2:

(a) Regression - Given a picture of a person, we have to predict their age on the basis of the
given picture

return to
refers to the property that we managed to predict the continuous value

(b) Classification - Given a patient with a tumor, we have to predict whether the tumor is
malignant or benign.

classify
This refers to

4 Unsupervised Learning - Unsupervised Learning

The input data for unsupervised learning is not labeled and its purpose is to classify the raw material in order to understand the internal structure of the material. It is further divided into clustering algorithms and non-clustering algorithms.

Clustering algorithms: can be used to organize large clusters of computers; can be used for analysis of social networks; can be used for market segmentation; can be used for analysis of astronomical data.

Classification according to genes

Non-clustering algorithms: the problem of cocktail parties.