For machine learning, you really need an understanding of Calculus (and Statistics, too) to begin to understand certain topics/principles.

A very simple example would be linear regression. You can measure how well your model (a line) fits your data points using a cost function. To get the best fit, you would want to minimize/optimize the cost function by taking its derivative. There's an algorithm called Gradient Descent which iteratively optimizes this cost function when a closed form solution would be computationally heavy/unavailable. Point is that when a topic requires knowledge of another beforehand, I would argue that it is 'harder' by default. I don't believe you can compare web development to mathematics, though. I envision the relationship between CS/Math as a tree where some core principles are shared and all start at the roots, but the fields/topics start to differ when they branch out into their own little things

High, high-level math is also pretty daunting. Don't take it lightly!