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Read what is a matrix, how to add, multiply and invert them, what is a determinant and what is an eigenvector and that's enough to get you started. There are many algorithms in ML where vectors/matrices are used mostly as a handy notation.
Yes, you will be unable to understand some parts of ML which substantially require linear algebra; yes, understanding ML without linear algebra is harder; yes, you need linear algebra for almost any kind of serious ML research -- but it doesn't mean that you have to spend a few years studying arcane math before you can open a ML textbook.
Who said anything about a few years? If you paid attention in high school, the linear algebra background you need is at most a few months' worth of work. I was providing a single counterexample, not saying that the full prerequisite list (which, if memory serves, is most of a CS curriculum for your average ML class) is always necessary.