It isn't everything you need know in 30 minutes, but it's a concrete coverage of lots of topics in machine learning in under 150 pages. Here's why I'm recomending this paper:
* The algoritm is easy to understand.
* It can handle classification, regression, semi-supervised learning, manifold learning, and density estimation. The paper gives an introduction to each of these topics as well as a unified framework to implement each algorithm.
* It can handle categorical data and missing data [2]
* It gives as good results as other state of the art algorithms.
* The paper is well-written and easy to understand for someone without a deep background in machine learning.
[1] It's mostly a review paper. Using random forests for density estimation is new.
[2] This review paper doesn't cover categorical data or missing data.
Is another great resource that introduces many ML topics from the ground up.
Fair warning, I haven't had time to have a look at the video (short break at work). I'll do it once I get home.
