If by &quot;college algebra&quot; you mean, abstract or modern algebra (groups, rings, fields, modules, vector space, lattices, etc), I would throw away the modern algebra book (for now: there are some applications of topology and topology-related parts of algebra, but without a solid grounding in analysis and numerical computing you won&#x27;t get anywhere) and get some analysis books. (baby (Principles of Mathematical Analysis) along with big (Real and Complex Analysis) Rudin if you want to do theoretical things, or just normal calculus books like Spivak&#x27;s if you want to do things). Then, learn computational linear algebra (I&#x27;m not appraised of too many great books in this, although try looking at the ee263 notes for Stanford, for linear dynamical systems (<a href="http:&#x2F;&#x2F;web.stanford.edu&#x2F;class&#x2F;archive&#x2F;ee&#x2F;ee263&#x2F;ee263.1082&#x2F;notes&#x2F;ee263coursereader.pdf)" rel="nofollow">http:&#x2F;&#x2F;web.stanford.edu&#x2F;class&#x2F;archive&#x2F;ee&#x2F;ee263&#x2F;ee263.1082&#x2F;no...</a>)<p>If by &quot;college algebra&quot; you mean that you learned basic algebra (fundamental theorem, equation solving, memorize solution to quadratic equation) in college, you need to learn calculus in one and then many dimensions (most of the people I know who are doing research-level things in machine learning learned this stuff in 6th to 8th grade, but who cares, especially if you are merely doing applications). Then, you learn linear algebra.