Hands-on with machine learning by Chase Davis.
From the webpage:
First of all, let me be clear about one thing: You’re not going to “learn” machine learning in 60 minutes.
Instead, the goal of this session is to give you some sense of how to approach one type of machine learning in practice, specifically http://en.wikipedia.org/wiki/Supervised_learning.
For this exercise, we’ll be training a simple classifier that learns how to categorize bills from the California Legislature based only on their titles. Along the way, we’ll focus on three steps critical to any supervised learning application: feature engineering, model building and evaluation.
To help us out, we’ll be using a Python library called http://scikit-learn.org/, which is the easiest to understand machine learning library I’ve seen in any language.
That’s a lot to pack in, so this session is going to move fast, and I’m going to assume you have a strong working knowledge of Python. Don’t get caught up in the syntax. It’s more important to understand the process.
Since we only have time to hit the very basics, I’ve also included some additional points you might find useful under the “What we’re not covering” heading of each section below. There are also some resources at the bottom of this document that I hope will be helpful if you decide to learn more about this on your own.
A great starting place for journalists or anyone else who wants to understand basic machine learning.
I first saw this in a tweet by Hanna Wallach.