It’s important to know what goes on inside a machine learning algorithm. But it’s hard. There is some pretty intense math happening, much of which is linear algebra. When I took Andrew Ng’s course on machine learning, I found the hardest part was the linear algebra. I’m writing this for myself as much as you.
So here is a quick review, so next time you look under the hood of an algorithm, you’re more confident. You can view the iPython notebook (usually easier to code with) on my github.