I’ve answered this question thousands of times.
I feel as though, because I repeat “prerequisites” so often, students stop believing me.
They wonder, “I took some other Udemy course on ML by some marketer guy and I learned so much! Why can’t you do the same?”
Unfortunately, they haven’t realized the truth: they didn’t learn machine learning, they learned some marketer’s interpretation of machine learning.
It didn’t lead to employable skills, and it didn’t lead to a proper understanding of ML.
What happens after?
Then you try to take a more advanced ML course (i.e. something at the college-level, or perhaps my courses).
Then you realize, “Hey, this is nothing like the course I took before. Why is there so much math?”
You expected all of ML to be as easy as the marketer’s course, because you didn’t understand that it wasn’t a real ML course.
I want to share a forum thread that I think should be useful, because instead of hearing it from me, now you can hear it from other people saying the exact same things.
The top comment in this discussion states the same topics I list in the prerequisites for nearly all of my ML courses:
- Calculus
- Linear Algebra
- Probability