At the core, its all about knowing:

- Probability
- Statistics
- Linear Algebra
- Calculus

My top resources for

- Probability and statistics :
- https://ocw.mit.edu/courses/mathematics/18-05-introduction-to-probability-and-statistics-spring-2014/readings/ (1.a to 6.c)
- https://www.probabilitycourse.com/
- https://www.statlect.com/
- Markov’s, Chebyshev’s and Chernoff’s bounds: https://youtu.be/bsOkMkxbl-k?t=2136

- Calculus : https://ocw.mit.edu/courses/mathematics/18-01sc-single-variable-calculus-fall-2010/
- Bayesian Analytics: https://www.coursera.org/learn/bayesian-statistics/home/info

The best way to start Machine Learning is Andrew NG’s flagship course: https://www.coursera.org/learn/machine-learning

Aside: I plan to make this a living document. I will update it as and when I find new resources.