Machine learning seems to depend on such math fields as probability, statistics, calculus, and linear algebra.
@pranav suggested discrete math would be an important prerequisite. However, someone else a discrete math book would be on a low priority if becoming a machine learning practitioner was my top priority.
Although I also want to become a software engineer as well as machine learning practitioner/researcher, I am a professional software engineer already, and I need to learn software engineering, math, and machine learning on my free time. If I was in a 4-year university curriculum, I would definitely start with discrete math.
How should I deal with discrete math? Should I learn it after probability, statistics, calculus, and linear algebra? Do I just skip it? Or, do I need learn it first?