Getting started with machine learning is easier than you think

If you think ML and AI are part of a sci-fi future, think again. The future is now.
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Date published
March 5, 2020

If you think ML and AI are part of a sci-fi future, think again. The future is now.

At our Microsoft Meetup in January, we hosted Seth Juarez, Principal Cloud Advocate and AI, machine learning, and quantum computing advocate—both within Microsoft and in the wider tech community. Seth talked about making deep learning accessible to all developers, with some simple mathematical models as examples that took the fear out of machine learning programming and made it easy for the devs in our audience to understand.

Seth outlined the differences between AI, Machine Learning, and Deep Learning, and explained that these are not the solutions for every problem—but when used appropriately, they can solve tricky problems, save time on repetitive-but-necessary tasks, and even do some pretty cool things.

Check out Seth’s talk, An Intuitive Approach to Machine Learning Models, which he has divided into four parts for his series, the AI Show.

Want to learn more? Rangler Harry Nicholls, in his blog AI for Frontend Developers, discusses the tools that democratize AI, including Microsoft’s Azure.

Both Seth and Harry make it clear: You don’t need tech for tech’s sake. Machine Learning can be as simple as building a recommendation engine, and the complexity should come from the scale, not the scope of the problem.
There’s more to learn about artificial intelligence for practitioners and business leads on our Artificial Intelligence page. There you’ll find resources and blogs to enhance your understanding of AI/ML, and how we implement the practice with our clients at Rangle.


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