MACHINE LEARNING

Detailed explanation-1: -Similar to finding the line of best fit in linear regression, the goal of gradient descent is to minimize the cost function, or the error between predicted and actual y. In order to do this, it requires two data points-a direction and a learning rate.

Detailed explanation-2: -Gradient descent, a very general method for function optimization, iteratively approaches the local minimum of the function. Since the loss function for linear regression is quadratic, it is also convex, i. e. there is a unique local and global minimum.