Hey, author here. It does sound kinda bigoted, doesn't it? Lemme clarify. I would really love for more people to learn this stuff. Truly. That's why I wrote a book to teach it to folks in Excel. What freaks me out a bit is that in order to enlarge the ML pie, a lot of vendors are trying to democratize machine learning not by teaching it to more people but by putting the gun in hands of folks who don't actually understand how supervised machine learning works and where it can go wrong.
I don't think people need PhDs to learn or do this stuff. But I think they need more than a "machine learning made easy!" app and a gung-ho attitude.
I feel that I'm halfway between main street and an ivory tower...not sure where that puts me. The upstairs bar at a pizza parlor?
Speaking as one of the people that phrase is probably pointed at, I don't begrudge the choice of words at all.
Lately there's been a big thrust in my industry to do just what you're worried about, and now that I've started to get an inkling of what's going on under the hood with predictive modeling tools over the past couple years it's starting to worry me.
And I'm starting to find their sales pitches to be even more condescending. "Don't worry about how this works or what it's really doing. Just pretend it's magic." It doesn't speak well for the vendors' opinion of their customers, and it leaves a lot of room open for legitimate technology to morph into silicon snake oil.
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?
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.
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.
This is the most condescending phrase I've read in a long time.
Otherwise the article had some good points, but that attitude really ruins it for me.