Everything You Wanted to Know About Machine Learning, But Were Too Afraid To Ask (Part One)
Everything You Wanted to Know About Machine Learning, But Were Too Afraid To Ask (Part Two)
by Charles Parker.
From Part One:
Recently, Professor Pedro Domingos, one of the top machine learning researchers in the world, wrote a great article in the Communications of the ACM entitled “A Few Useful Things to Know about Machine Learning“. In it, he not only summarizes the general ideas in machine learning in fairly accessible terms, but he also manages to impart most of the things we’ve come to regard as common sense or folk wisdom in the field.
It’s a great article because it’s a brilliant man with deep experience who is an excellent teacher writing for “the rest of us”, and writing about things we need to know. And he manages to cover a huge amount of ground in nine pages.
Now, while it’s very light reading for the academic literature, it’s fairly dense by other comparisons. Since so much of it is relevant to anyone trying to use BigML, I’m going to try to give our readers the Cliff’s Notes version right here in our blog, with maybe a few more examples and a little less academic terminology. Often I’ll be rephrasing Domingos, and I’ll indicate it where I’m quoting directly.
Perhaps not “everything” but certainly enough to spark an interest in knowing more!
My take away is: understanding machine learning, like understanding data, is critical to success with machine learning.
Not surprising but does get overlooked.