Wherever you go (I second the Khan Academy recommendation), you'll need to become familiar with at least basic linear algebra (vector spaces, linear independenc

Wherever you go (I second the Khan Academy recommendation), you'll need to become familiar with at least basic linear algebra (vector spaces, linear independence, spanning, bases, matrices and their operations) and multi-variable calculus (partial derivatives, the chain rule [and other basic derivative rules], the gradient vector, and gradient ascent/descent for finding the local maximum/minimum of a function) before you can really understand machine learning and backprop.