Every minute, a new data science course is born somewhere. Granted, I totally made this up but I might not be completely wrong. Lots of research groups are doing breakthroughs in data science and artificial intelligence everyday, and yes, there is hype, but there is also optimism, strongly supported by evidence.

So, is it really time to jump in and drop one’s career and everything to switch to AI? Is it finally time to join Coursera/Udacity/Edx/your favorite MOOC/university?

Well, no.

The thing is, AI and ML, hype or not, are tools. They are hammers waiting for nails.

It doesn’t make much sense to invest in machine learning training by itself. Successful applications of machine learning and, in general, artificial intelligence, often require solid domain knowledge and proprietary data, one way or another.

Knowledge is certainly transferable among machine learning applications, and solving one type of problems (yes, even mildly artificial, like Kaggle) does help to solve unseen problems, and you can get lucky and apply out of the box algorithms with default parameters and match the state-of-the-art in a particular domain, or exceed it. But going beyond the easy-hanging fruit takes tons of effort and a combination of the above.

A better career strategy might be choosing a domain one is interested in, and then see how AI can be applied there. And for this, you just need to skim through some of the excellent courses around, no need to get a PhD in machine learning.

 

 

 

Coursera AH Purple Design 2

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