Supervised Machine Learning - Overview and Examples

                                                        Supervised Machine Learning

Supervised Machine Learning is a type of machine learning in which we have both input and desired Output data are provided. Input and Output data are labelled for classification and regression to provide a learning  so that we can predict an output in future.

How does Supervised Machine Learning Work?- In Supervised learning we have an Input variable X and an Output variable Y and we use an algorithms to find the mapping function from the input(Input may be more than 1) to output as below:-
                                                        Y=f(X)
The Ultimate goal is to approximate the mapping function so that if we have new input data(X) we can predict an output(Y).

Why it is called Supervised?- Because the learning algorithm from the training data sets can be thought as a teacher supervising the class. The algorithms makes predictions from the training data sets and is corrected in case of wrong prediction. Learning stops when we achieved acceptable performance. 

Algorithms used in Supervised Machine Learning:- There are lots of Algorithms used. Some of are listed below:-
1. Linear Regression
2. Logistic Regression
3. Support Vector Machine
4. Decision tree
5. K-Nearest Neighbor
6. naive bayes.

Real world Examples of Supervised Machine Learning:-

1. To predict the price for House:- Suppose we have input data X(square footage, number of bedrooms, number of floors, Area, Year of build ) we can predict an output Y(price of that house).

2. To Predict the price of an specific stock.

3. To Predict the Loan Availability of an customer on basis of their credit history.

4. Spam email filter.

5. To predict an increase in Cell Traffic(In case of Telecom).

There are so many examples in real world. Wherever we have labelled input  and we have to predict an output we can use Supervised Machine Learning.