Just wait until you start interviewing at even crazier companies than FB/Google that would like you to tell them 20 ML algorithms, 20 computer vision algorithms, compare expected results of non-linear optimizations applied to some weird Deep Learning problem and analytically justify it (why is it worse/better than SGD on this loss function?), then off to some deep domain knowledge, conjure graph algorithms on the spot on problems you've never seen before, all in 1.5h, expecting Stanford/MIT PhD-level answers.
Sharing a data point: I've interviewed with Google multiple times and at no point during the interview was I asked a question that required an ML/CV/DL algorithm.
I suspect those would be more appropriate for a 'data scientist' / 'researcher' position - for a generalist, the interviews were very standard and not crazy at all.
In hindsight, if I had taken the time to review basic CS algorithms ahead of the interview, I would have done much better.
That said, I'm not sure I'd be a better employee at Google if the only difference between my being hired or not hired is the fact that I reviewed some basic data structures and algorithms.
