I know enough about machine learning (and scikit-learn) to make simple models with production deployments. The results are pretty good, but they come almost exclusively through trial and error—testing different algorithms and approaches until something works.
Unfortunately, I really don't understand the underlying theory. Every time I start diving into it, I realize it'll probably take a huge amount of time to understand (particularly because I'll likely have to do an intensive refresher on linear algebra and multivariable calculus).
How dangerous is it to be practicing machine learning without deeply understanding the theory? Is it worth the substantial time investment?
I know enough about machine learning (and scikit-learn) to make simple models with production deployments. The results are pretty good, but they come almost exclusively through trial and error—testing different algorithms and approaches until something works.
Unfortunately, I really don't understand the underlying theory. Every time I start diving into it, I realize it'll probably take a huge amount of time to understand (particularly because I'll likely have to do an intensive refresher on linear algebra and multivariable calculus).
How dangerous is it to be practicing machine learning without deeply understanding the theory? Is it worth the substantial time investment?