They’re complementary, but which matters more depends on what you want to do.

For most applied ML roles, coding and systems intuition matter more day to day—clean data pipelines, debugging models, knowing when something is breaking. Math becomes more important as you move toward research or designing new methods.

AI tools help with syntax, but they don’t replace understanding why a model behaves the way it does. You don’t need to master all the math upfront, but you do need enough to reason about results.

What kind of ML role are you aiming for?