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This post is definitely not an 101. It says "Move the cursor below to change the value of λ :" but never tells who alpha is. Not newbie friendly.

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It's lambda - basically the amount of regularization (simplification or 'overfit penalty') to impose. Usually chosen by cross-validation - try every lambda between say 0 and 10 in 0.5 increments and choose lambda that gives the model the lowest error cross validation measure...

OP's point (and it's valid) is that the author covers the regularization term but does not explain the task aside from the following throwaway line:

> "The goal is to find the model that minimzes (sp) this loss function."

Unless we're overloading the word "model" -- which is only going to confuse your intended audience -- this statement is incorrect. We're actually searching for the parameterization \alpha of the model that minimizes the loss function.

It actually explains what alpha is. The line above its first occurrence it says: "In the case of polynomials". So alpha are the coefficients of the polynomial.

Rhetorical question: what's so special about the polynomial coefficients when fitting a model?

This isn't addressed in an article about regularization, yet it's a key point.

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