5 Simple Tips to Supercharge your Machine Learning Practice

Since I was in high school, I've had this weird obsession of squeeze the key concepts of everything that I learn in one page. Looking back, that was probably my lazy mind's way to get away with the least amount of required work to pass an exam…but interestingly that abstraction effort also helped a lot to learn those concepts in a deeper level and to remember them longer. Nowadays when I teach Machine Learning, I try to teach it in two parallel tracks: a) main concepts and b) methods and theoretical details, and make sure my students can look at each new method through the lens of the same concepts. Recently I got a chance to read "Machine Learning Yearning" by Andrew Ng, which seemed to be his version of abstracting some of the practical ML concepts without getting into any formula or implementation details. While they can see so simple and obvious, as an ML engineer I can attest that losing sight of those simple tips are among the most common causes for an ML research to fail in production, and being mindful of them is what distinguishes a good data science work from a mediocre one.