Seconding and elaborating on this: If your technique is underperforming, you'll be in a position to find out why instead of just throwing your hands up. You'll be better able to select which technique to use. You'll understand what the parameters mean and be able to select and adjust them in a principled way instead of through trial-and-error. In short, you'll build better models in less time if you understand the underlying theory.
ML "techniques" are algorithms too, so they are a subset of algorithms you can learn.
Is it worth learning them? Yes. Is it worth learning them to the exclusion of "classical" algorithms? Probably not.
But if you want ML knowledge that is almost certain to be just as useful in 5 years as today, the best thing you can do is study the fundamentals - probability, statistics, linear algebra.
