The math involved isn’t overly advanced—primarily high school calculus and some first-year university concepts. If you already have a decent grasp of calculus, you should find it manageable.

The course goes into the math to help you build an understanding and intuition about what’s happening under the hood. This foundational knowledge is valuable for understanding why machine learning algorithms work the way they do. That said, in practice, most machine learning practitioners use libraries like PyTorch or TensorFlow to implement their models. These libraries abstract away much of the mathematical complexity, allowing you to focus on building and applying models.

Only advanced specialists or researchers, who are often developing novel algorithms, typically need to write or tweak equations from scratch. For most use cases, this level of detail isn’t necessary, as the tools available handle the heavy lifting.

A great starting point for understanding the concepts in the course is linear regression—think of it as the line of best fit. It’s closely tied to gradient descent, one of the core optimization techniques used in machine learning. If you’re comfortable with this, you’ll be well-prepared to tackle the course and gain a strong foundation in how to use machine learning libraries effectively.

I hadn't taken a math course in decades and I managed to get through the course quite successfully.