You could do worse than go back and make sure your foundation maths is
solid. Revise some Discrete Maths books and understand identification,
classification, sets, equivalence... Make sure you've a solid ground
on concepts like dimensions, functions, differentiation, integration,
extrapolation, interpolation, then toughen up your Linear Algebra,
optimisation, solving, regression, before getting into approximation
and gradient descent.
Sure, most of this sounds as dull as a broken clock, but in my
observation it makes the difference between students who can just
use machine learning tools by copying textbook cases and adopting a
lot of fancy new terminology, and those that understand what they're
doing.
That difference really kicks in once you get off the beaten track of
popular use-cases, into applying ML to new, unproven applications.
Then you need a deeper understanding of why some algorithms may be
useful and others are inappropriate.
Sure, most of this sounds as dull as a broken clock, but in my observation it makes the difference between students who can just use machine learning tools by copying textbook cases and adopting a lot of fancy new terminology, and those that understand what they're doing.
That difference really kicks in once you get off the beaten track of popular use-cases, into applying ML to new, unproven applications. Then you need a deeper understanding of why some algorithms may be useful and others are inappropriate.