Calculus provides the foundation for finding the optimal solutions to the math problems you set up. This is through a few methods: differential curvature for gradient descent, and the theory of real functions provides gaurantees regarding convex functions and more generally, where optimal values can occur (exterma and stationary points).
Linear algebra is in general the language used to set up and solve the problems systematically (on a computer).
Probability and stats build on calculus to provide formal methods to formulate the problems. ML adds techniques to this area to tackle problems focused around getting machines to learn and solve specific domain problems.
Deeper math is probably more necessary when developing machine learning algorithms. To apply machine learning techniques, less math is required. But knowing Lin algebra and matrix manipulations (Matlab, R fluency, etc) will not be wasted effort either way.
Linear algebra is in general the language used to set up and solve the problems systematically (on a computer).
Probability and stats build on calculus to provide formal methods to formulate the problems. ML adds techniques to this area to tackle problems focused around getting machines to learn and solve specific domain problems.
Deeper math is probably more necessary when developing machine learning algorithms. To apply machine learning techniques, less math is required. But knowing Lin algebra and matrix manipulations (Matlab, R fluency, etc) will not be wasted effort either way.