It feels like everyone is talking about AI these days – and what’s worse, everyone seems to have a different term for it! “Deep Learning”, “Machine Learning”, “Artificial Intelligence”: what do they all mean, really? Are the concerns about ethical algorithms and automation of jobs grounded in fact, or are they overhyped? And what language will SkyNet be implemented in? (My money’s on JavaScript.)

In this session we’ll answer those questions – and walk through what machine learning really is: how to build a model by ingesting data, training on it, testing, and then deploying to production. We’ll also discuss which tools are primarily used by data scientists; how much math is really required to run predictive models; and some strategies you can use today to incorporate artificial intelligence into your applications.