Machine Learning

Basic to advanced

Linear regression

Trying to map input to output assuming there’s a linear relationship, a normal relationship follows the line. line for 2D, plane for 3D, hyperplane for higher dimensions. Line separates the region in two for 2D, so does plane for 3D and so on…
f(x) = mx + b; where m = slope, b = bias or y intercept.

Vector form

Linear regression for higher dimensions (hyperplane)

In higher dimensions the linear regression is a hyperplane.
Sum of Squared error: SSE is the loss function. The co-efficients can be found by Matrix decomposition, Gradient descent.