Classifying images into Cats or Hotdogs may make for great AI demos, but for many of us, it has limited $DAYJOB uses. What if you have loads of documents, or inconsistently-written text, or a mash of information? Fear not - the latest AI / ML techniques for text can help!
With the help of Apache MXNet, scikit-learn, ElasticSearch and friends, we'll progress from a simple text-based ML system, to an advanced system with full linguistic understanding. We'll also cover some key concepts around building AI / ML systems, and some of the pitfalls that beginners risk encountering. Full example code provided!
Along the way, we'll look at why text has historically been hard AI / ML, what the latest techniques are, and then Open Source libraries / frameworks implementing them.
Thanks to the magic of cloud-hosted notebooks, you can follow along with the code as we go, and try some live coding if we all dare!