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.

Machine Learning Overview and its Uses in Telecom

Today we are discussing about the very Hot topic for nowadays  which is Machine Learning. First we will find what is Machine Learning, why we need Machine Learning ,Types of Machine Learning and how Machine Learning can help Telecom.

What is Machine Learning?- Idea behind the machine learning is to make machines(computer) capable of taking decision without explicitly programmed means once machines are trained and new data comes in the system then machine can train themselves without reprogrammed again.

Why we need Machine Learning?- There are lots of data generates in every field nowadays and that data too is present in the form of structured and unstructured. With processing by Humans for these data are not so easy so these data can be used for training the machine and machine can take efficient and fast decision. Machine is also do fast computation and complex thing which human cant do.

How does Machine Learning Work?- Machine Learning is a technology in which machines learns through training( We have to train a model with training data sets often known as inputs) and processes specific input to predict an output. In order to get desired output we have to properly trained the model with help of some parameters/Algorithms.

Uses of Machine Learning in Telecom or how can Machine Learning will help Telecom?- The telecom operators have already the masses of data in terms of its customer data, Network performance data, network traffic data and social media data. So there are some Machine Learning applications which are helping telecom:-

1. Using Machine Learning to identify restart the sleeping cells:-  In this application we can analyse from network performance data to identify the sleeping cells and trigger an automatic restart.

2.To identify potential churners:- The customer churn is highest in the Telecom in comparison to other fields, so for the telecom operators it is very important to retain there customers. With help of Unsupervised Learning( We have customer behaviour data is already with us) we can identify the potential churners and plan accordingly.

3.Using Machine Learning to improve customer service application.

4.Using Machine Learning to identify fraud mitigation.

There are different types of Algorithms used in machine Learning as listed below. We will discuss each one in next blog.