Great article! Reblogging it for future reference.

Machine learning is broadly split into two camps, statistical learning and non-statistical learning. The latter we’ve started to get a good picture of on this blog; we approached Perceptrons, decision trees, and neural networks from a non-statistical perspective. And generally “statistical” learning is just that, a perspective. Data is phrased in terms of independent and dependent variables, and statistical techniques are leveraged against the data. In this post we’ll focus on the simplest example of this, linear regression, and in the sequel see it applied to various learning problems.

As usual, all of the code presented in this post is available on this blog’s Github page.

## The Linear Model, in Two Variables

And so given a data set we start by splitting it into independent variables and dependent variables. For this section, we’ll focus on the case of two variables, $latex X, Y$. Here, if we want…

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