How Do I learn Mathematics for Machine Learning?

To have a basic mathematical background, you need to have some knowledge of the following mathematical concepts:

• First things first — the prerequisites:
• Basic calculus. In Machine Learning, you’d be working on a lot of optimizations that require knowledge of Calculus. It would be highly recommended that you are aware of functions, limits, differentiation, maxima, minima, etc.
• Linear Algebra. When you talk about ML, you will be dealing with matrices and vectors every day. So, knowledge of Linear Algebra is a must. However, you’d also be required to know about other important topics like Eigenvalues and Eigenvectors.
• Probability. Most ML algorithms try to “model” the underlying phenomena that generated the observed data. All of this modelling is probabilistic. It is therefore highly recommended that you are comfortable with the theory of Probability.

other Topics in mathematics include-
- Multivariable calculus
- Functional analysis (not essential)
- First-order logic (not essential)
You can find some reasonable material on most of these by searching for “<topic> lecture notes” on Google. Usually, you’ll find good lecture notes compiled by some professor teaching that course. The first few results should give you a good set to choose from.

For instance, here are some videos that I just found on Youtube for Clearing the basic concepts of maths-

[Machine Learning] Practical Implementation of Linear Regression (2019) | Eduonix

[Machine Learning and AI] | Application of Mathematics for Machine Learning 2019| Eduonix