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What is Supervised Machine Learning? || Supervised Machine Learning techniques and algorithms.

What is Supervised Machine Learning? || Supervised Machine Learning techniques and algorithms.

supervised machine learning
supervised machine learning is one of the method of machine learning which uses
various machine learning mathematical techniques to train the machine using data which is already labeled. someone can build supervised machine learning
algorithms from labeled training data to predict the same.

Some of the examples of supervised machine learning systems are self-driving cars,
expert systems, facial recognition programs, chatbots and robots which support future judgments.

following are the mathematical techniques that are used in building supervised 
machine-learning algorithms.

1) Linear Regression
2) Logistic Regression
3) Decision Tree
4) Random forest
5) support vector machine

Linear Regression: 

The linear regression technique uses a linear mathematical formula/technique to predict the dependent
variable or output value. let us consider the following area vs price linear graph in order to understand
the Linear Regression technique. 

        y=m*x + b

where y=dependent variable
           x=independent vairable
Here the area is the independent variable which is plotted on the x-axis and price is the dependent variable plotted on the y-axis. And the value of the dependent variable (i.e price) is predicted by an above linear mathematical formula i.e y=m*x + b.

Linear Regression is further classified into single variable Linear Regression and 
multiple variables linear Regression depending upon the number of independent variables.

Logistic Regression:

logistic regression  method uses logit  mathematical function i.e
                                        f(x) = 1/(1 + exp(-x))

which helps to predict the probability of occurrence of an event by fitting data to this
logit function/(f(x) = 1/(1 + exp(-x))). And this logit function always predict the output value whose values lies in between 
0 and 1.

Logistic Regression further classified into binary classification and multiclass 

the binary classification has only two distinct classes i.e binary classification
prediction is like true or false i.e it has only two options.

And selecting between more than two classes is called multiclass classification.

Decision Tree:

the decision tree technique is a flowchart in which test on a feature is represented 
by each internal node and each leaf node represents class label and branches 
represent conjunctions of features that lead to those class labels.

and the syntax for the same looks like as above.

Random Forest:

the supervised machine learning algorithm which is used for both classification 
as well as regression is known as random forest

The basic syntax of the Random Forest algorithm is explained above with some code programmed in
anaconda navigator

Support Vector Machine:

support vector machine is another supervised machine learning technique which is used for solving
highly non-linear problem using a kernel technique

support vector machine is abbreviated as SVM which is also known as a support vector network- a very powerful method of solving both linear and regression problems. In short, SVM is a discriminative 
classifier which aims at finding decision boundaries that separate observations with differing class

the basic syntax of the support vector machine algorithm is shown above which is designed/programmed 
in anaconda navigator.


  1. Well explained... Easy to understand. . Thanks a lot for sharing your knowledge toward data science


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