What is the difference between least squares method and mean squared method in calculating the error? I think I am a little bit confused between the LSE (Least Squared Error) and the MSE (Mean Squared Error). So how can these two methods differ in calculating the error of the linear regression model?
And if available, please provide the mathematical equation of both methods.

Least squares minimizes the sum of the squared errors between the actual value and prediction of model. This is used to find the best fit line using Gradient Descent Optimisation. The line which gives the minimum least squared error is known as best fit line
LSE =  Sum over all observation((Y_actual - Y_predicted)**2)
While MSE is mostly used as evaluation error and formulae is given below

