My Journey into Machine Learning: Class 5 (Regression)

In the third article, I introduced the core concept of Linear Regression. To recap, we want to have a function f that models our data. We build an approximator to the function f called g. We use an Error Function in order to measure how good our function approximates the data. The value of our error function is not that great. The closer the value to 0, better our model will be.