Machine learning is everywhere these days. Features like search, voice recognition, recommendations – they’ve become so common that people have started to expect them. They’re starting to expect the apps we build to be smarter.
Ten years ago, machine learning and data mining techniques were only available to the people dedicated enough to dig through the math. Now that’s not the case.
The most common machine learning techniques are well known. Standard approaches have been developed. And, fortunately for us, many of these are available as ruby gems. Some are even easy to implement yourself.
In this presentation we’ll cover five important machine learning techniques that can be used in a wide range of applications. It will be a wide and shallow introduction, for Rubyists, not mathematicians – we’ll have plenty of simple code examples.
By the end of the presentation, you won’t be an expert, but you’ll know about a class of tools you may not have realized were available.
Ben has been developing web apps and building startups since ’99, and fell in love with Ruby and Rails in 2005. Before co-founding Honeybadger, he launched a couple of his own startups: Catch the Best, to help companies manage the hiring process, and RailsKits, to help Rails developers get a jump start on their projects.
Ben’s role at Honeybadger ranges from bare-metal to front-end… he keeps the server lights blinking happily, builds a lot of the back-end Rails code, and dips his toes into the front-end code from time to time.
When he’s not working, Ben likes to hang out with his wife and kids, ride his road bike, and of course hack on open source projects.
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