New to Machine learning — learn Linear regression basics — made simple

To make learning simple:

Linear regression explains the relationship between 2 variables = X and Y

X is the independent variable

Y is the Dependent variable

For a set of X,Y values, which is the input data set. Once we create the regression line, we can predict a Y value for a input X value.

The formula to predict Y value is : [Y = mX + c]

m is the slope of the Line and c is the intercept

• **[[ During training the Model, the Model calculates the optimum value for m and c variables — This is the crux of Machine learning algorithm]]
• The Model will use the m and c values to predict Y for X input.

Least squares method is used to predict the values of m and c variables.

For the below equation — e is the error component in prediction of Y

Y = mX + c + e

In Machine learning workflow, we use Linear regression Algorithm in Supervised learning space. we train our Model with Input dataset of X,Y values.

Please watch these videos to understand Linear regressions concepts: