Three common problems on supervised learning

A: They are almost identical. Linear Regression uses Ordinary least squares (OLS) to get an unbiased and high variance solution. Things like multi-collinearity can cause Linear Regression to fail. Ridge Regression is solved pretty much the same way, but it adds a regularization constant. The constant is a source of bias and can decrease variance.