My Journey into Machine Learning: Class 3 – Towards Data Science

As we discussed in the first article, linear regression is a supervised learning algorithm where the output is continuous valued. Think of r t as the output and X t as the training examples. This is the ideal scenario that we would like to have: A function that predicts the output perfectly from the training examples. But this does not generally happen in the real world. There is an additional noise that needs to be added to the function to get the required output.