I've deleted all the paragraph markers, since it wasn't hard to do and they aren't needed.

I'm making a bunch of small changes which you might want to review, but here are my main questions (or requests):

1)

> An extreme example of preprocessing is explicitly forming new features in the data.

For some reason, "forming new features" seems a bit jargonesque to me, perhaps because it has a plain English meaning which isn't what you mean. "Forming a new feature" sounds like growing a nose, or the formation of a volcano, or creating something that wasn't there. But I guess what it _means_ is "computing a complicated function of the data, which lets us discern important features that were hiding in it."

So maybe a word of explanation would be nice. You illustrate it with an example that will make sense to people who have been carefully following the El Niño series, but a simple definition would be nice first - like:

> An extreme example of preprocessing is explicitly 'forming new features' in the data: that is, computing a function of the data that lets us discern important features.

(It's nice to teach people the jargon of the trade, so I'm not suggesting that you get rid of this term.)

2)

> One factor that's often related to generalization is href="http://en.wikipedia.org/wiki/Regularization_%28machine_learning%29">regularization and in particular sparsity.

I'm glad you're bold-facing defined concepts, but you never really define regularization here. It would be good to either define it or skip it; some readers may click on the link but not many. You only really discuss sparsity.

I use single quotes for terms to indicate "you may not know what this means yet, but don't worry, I don't expect you to know what it means". So if you don't want to explain regularization you can say

> One factor that's often related to generalization is href="http://en.wikipedia.org/wiki/Regularization_%28machine_learning%29">'regularization' and in particular sparsity. (then, explanation of sparsity)

3) You also don't explain "identifiability", but I just put single quotes around this, and it's already in a parenthetical remark so people will know they can skip this remark if they want.

That's it! All in all, a remarkably clear and inviting introduction to some big ideas! Thanks!