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Machine Learning

Machine Learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
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Smashing Magazine Icon Smashing Magazine

Making a mobile app with facial recognition features

This article isn't a how-to, per se. It's more like a research report written after attempting to build such an app for the first time. There's nothing wrong with that, though, and this write-up is super useful if you're about to tackle a similar problem space. Open source libraries are tried, facial recognition services are evaluated, and their takeaways are solid, if not a bit disappointing. As you can see, the really simple idea of using facial recognition functionality was not that simple to implement. The entire piece is worth a read.

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Analyzing GitHub issue comment sentiment with Azure

If you've been looking to dabble in some AI and serverless, Phil Haack shared his process to create a SentimentBot for GitHub issues with Azure Functions. Perhaps the combination of machine learning and human judgement could make the problem more tractable. I decided to play around with Azure Functions because they have specific support for GitHub Webhooks. GitHub Webhooks and Azure Functions go together like Bitters and Bourbon. If you want to skip the code and just test it out, head to this issue.

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What's the difference between data science, machine learning, and AI?

We've needed this post for a very long time. Thank you David Robinson. When I introduce myself as a data scientist, I often get questions like “What’s the difference between that and machine learning?” or “Does that mean you work on artificial intelligence?” But that overlap, tho. The fields do have a great deal of overlap, and there’s enough hype around each of them that the choice can feel like a matter of marketing. But they’re not interchangeable. Most professionals in these fields have an intuitive understanding of how particular work could be classified as data science, machine learning, or artificial intelligence, even if it’s difficult to put into words. Here's the break down...

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