# Machine learning —making Supervised learning simple to understand

Using ML notations:

Single Training set (x,y) = x is Feature and y is Target

We are trying to Predict value of y for input Feature instance [data set].

y = mx+c

m = Number of training instances

x = “input variable” Feature

y = “output variable” Label\Target

**ML workflow :**

Training Set->input to->Learning Algo->creates->Model[Prediction Function] -> give Feature -> predicts Target

#### function = (x) -> y

The Model [function()] takes a new Feature as Input and returns a predicted Target value.

Model = function(Feature set [x]){

returns Estimated Target value [y]

}

**y is a linear function of x**

y is a straight line function of x

Linear regression with One or Multiple Variables[x].

Univariate means One variable[x]