Linear Regression
This is one of machine learning’s lightly interpreted algorithms out there. Often always taught using abstractions that kills the beauty of this interesting algorithm. Let’s look at this algorithm from the ground up. First for any supervised machine learning, we need a dataset. Let’s construct a simple one. $$ (x_1, y_1), (x_2, y_2), (x_3, y_3)...(x_n, y_n) $$Here, $x_i$ is the input to the algorithm and $y_i$ is the output from the algorithm. Practically, the input $x_i$ can of arbitrary dimension belonging to $\mathbb{R}^m$, where m is the number of independent features in the input. But, for simplicity we are going to work with $x_i$ belonging to $\mathbb{R}^1$. ...