If you already know partial derivatives, gradient descent algorithms, and some linear algebra, it's easy. The assignments are not challenging either, even compared to other coursera classes. But for people that don't have this background, it can be hard.

I think the problem with Coursera is that they try to accommodate as many students as possible, which means the classes aren't very deep. I don't think this should be the role of a ML class to teach about partial derivatives or matrix factorization for instance. It's great that the class is accessible to many people, but sometimes you feel that you just scratch the surface, and that you're not at the level of university students that went through more challenging classes.

As a comparison, I did the labs of 6.828 and 6.824 (OS and distributed systems) on MIT opencourseware and it was much harder and much more rewarding than any coursera class. And I did only the labs, there is much more material to cover to do it seriously (quizzes, lot of articles to read...). Kudos to MIT students! (I wonder how many such classes they take during one semester). Too bad opencourseware classes aren't always properly set up for outside students. There may be some material missing, no forum to ask questions, video quality can be very bad and so on...

Here is the stanford cs229 course, which is a proper deal more in depth: https://see.stanford.edu/Course/CS229

Assuming you're taking about Andrew Ng's Stanford ML course, I had the same impression until I realized that the actual Stanford ML course is a lot harder and theory-heavy. Basically, the Coursera thing is a more practical, watered-down (less math) version of the real Stanford course (which btw you can still find on Youtube, though it may be a bit dated)...

I'm guessing the author hasn't a maths education beyond high school.