[shostack]

I just recently posted in a HN machine learning thread asking for beginners resources. This sounds right up my alley.

I'm teaching myself Ruby (and other stuff), but consider myself pretty advanced with Excel and web analytics in general. This seems like a great way for me to get my feet wet in the deeper science of things with tools I'm already very familiar with (moreso than Ruby at least).

John, can you clarify a bit on how much background is needed in various areas of math to get the most out of this book? Or do you feel you do a solid job of teaching that as one progresses through the chapters?

[mistermcgruff]

A semester of linear algebra (or just a willingness to Wikipedia a few things) plus Excel experience is all you need.

That said, the book does require a lot of effort, because the techniques are worked through step by step.

But once you learn all the guts of the algorithms, you never have to implement them again! The last chapter moves the reader into R package land with the confidence that you now know what those packages are basically doing and what to watch out for.

[mistermcgruff]

If you've got a college semester of linear algebra under your belt (or equivalent) and are pretty good with Excel, then the book is a good fit. Even the algebra can be optional if you're willing to use wikipedia liberally. I don't take for granted that the reader has a lot of background.

That said, there are parts in the book that are really quite hard. Hard in that they just take time to work through. Because the book is about learning all the steps that go into training models and doing analyses from scratch. But once you do it all from scratch once, you don't necessarily have to ever ever do it again.

It's taught in Excel for learning purposes, and then the last chapter moves you into R. Literally, the Holt Winters forecasting chapter of the book is 50 pages while in R it's the forecast package plus 3 lines of code.