Want to increase your workplace value? Acquire machine learning skills


Machine learning isn’t a future technology: It’s here now. Do a Google News search on “machine learning” on any random day, as I did, and you’ll see headlines such as:

  • How Machine Learning is Helping Morgan Stanley Better Understand Client Needs
  • High schooler makes 3D-printed, machine learning-powered eye disease diagnosis system
  • Moving forward for machine learning for cybersecurity
  • Active machine learning for the discovery and crystallization of gigantic polyoxometalate molecules (geek alert!)
  • Google and MIT’s new machine learning algorithms retouch your photos before you take them
  • Facebook will use machine learning to fight fake news
  • Machine learning comes to your browser via JavaScript

Finance, medicine, cybersecurity, digital photography, Internet browsers, and even the battle against “fake news” — it’s like machine learning is everywhere!

This makes anyone with mad machine learning skills (or even just proficient machine learning skills) valuable to enterprises in the digital economy. “If you’re looking for an in-demand career, setting yourself up with the skills to work with smart machines/artificial intelligence is a good move,” writes Learn to Code With Me creator Laurence Bradford in her recent Forbes column, 8 Ways You Can Succeed In A Machine Learning Career.

Bradford and Dataiku CEO Florian Douetteau offer plenty of useful advice, from the obvious but important (understand what machine learning is and maintain a sense of curiosity) to the more specific (such as “learn Python” and “translate business problems into mathematical terms”). That’s a tall order for the poets among us but, as Bradford notes, machine learning “is a field practically designed for logical minds.”

That being said, machine learning professionals cannot operate in splendid isolation and expect to be effective (so take off those headphones!). Douetteau nails it: “Today, when you are working in machine learning, you are most likely working as part of a team, and this team would comprise people who have direct interaction with the business. So it means if you want to be successful as a machine learning practitioner today, you must be ready and able to interact with the business and be a team player.”

She and Bradford offer several other pieces of useful advice for people considering machine learning as a specialty. If you want to learn more there are plenty of online resources about machine learning and artificial intelligence. Here’s a collection of links provided by Hacker Noon, and here’s another posted on Medium.


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