The most frequent question I get from developers is: what is the best way to get into Machine Learning?
A few years back, my response was:
- Google for best resources and learn
- Find problems at work and apply what you learn
Though that response was honest and sincere, I realized soon that it was taken as a motherhood statement. So as a more concrete reply, I narrated my path in an article: An Engineer’s Trek into Machine Learning
, and I shared the link whenever someone asked.
I genuinely thought I have simplified it with that article but it was still not simple enough. The reason is fearful analysis paralysis:
- Material available on the internet for learning ML is overwhelming.
- Somehow it gives an impression that you must first master hard math, and that scares away people.
So, let me first say that I do NOT know what is the BEST way. but I know a reasonably good way. And starting on a reasonable good path is much better than looking for the best path.
If you are a developer, and you prefer learning by doing, here is a path that optimizes for speed in acquiring functioning knowledge. It first does a breadth-first scan of ML techniques and applies those to problems, and then explore deeper based on the problem at hand.