I've yet to hear a financial services executive focus on machine learning as a key part of his company's insight strategy.
"Analytics" still dominates Google searches (not only ahead of "machine learning" but far ahead of even "big data"):
Businesses are increasingly looking to hire data scientists, and they leave universities having been taught machine learning together with a mixture of statistics and computer science. When I spoke with data science students at an event in Edinburgh
, it was clear they saw machine learning as a key part of their specialty, even if most businesses rarely mention the term.
In the 20 years since I was an R&D manager developing artificial intelligence pilots, I've seen few businesses even attempt to apply the techniques I found so powerful (including case-based reasoning, neuro-fuzzy logic and genetic algorithms). But perhaps data science finally has enough momentum to take AI into mainstream commercial application.
So, if you're looking to keep up with developing data science or (wider) customer insight professions, what should you know about machine learning? Is it too late for you to learn? Do you need to return to university?
Although the social life options of the latter may sound appealing, most leaders don't have time to put their corporate careers on hold while they retrain. Luckily, there are online resources to help you get up to speed and, at least, understand the language being used by your latest hires. In this post, I'll share a few online resources and reviews I hope you'll find useful.
See Also: How Machine Learning Changes the Game
What better place to start than an online tutorial that claims to be the world's easiest introduction. With the catchy headline "Machine Learning is Fun!", this two-part blog—published on Medium by Adam Geitgey
—is perhaps not as simple as some would like, but it does provide a useful overview of techniques.
To balance the data science perspective on machine learning, I thought it might also be interesting to share a market research perspective. This balanced and useful review by Kevin Gray
in Quirks provides such a perspective. It should help researchers consider where AI algorithms could also be applicable to their quant work.
If all that education and advice has made you keen to get your hands dirty and try machine learning, the next question is how you can get started. Well, if you are an R coder or have analysts in your team with R programming skills, here's a handy starting point shared by Jason Brownlee
Don’t worry if you can’t, or prefer not to, use R. It seems that, as well as a plethora of machine learning tools, there are some heuristics, too. In this quick-start guide from the same site as above, Brownlee also shares how to understand any machine learning tool quickly (the information is so good I had to include this second link from the same blog.
Finally, to really get you ruminating on the subject, consider this more philosophical piece by Christopher Nguyen
, where he explores our relationship with AI the other way—what can the ways machines learn teach us about our own brains, imaginations and the role of intuition. Thought-provoking stuff
I hope this post was of interest. If you’ve discovered other great content online that can help us all better understand machine learning, please do share.
Have a great time learning more!