Almost everyone who comes to machine learning from outside a quantitative background hits the same wall, the moment a tutorial casually drops a gradient, an eigenvector, or a probability distribution and assumes you are fine with it. This specialization exists to take down that wall, and it does so with a clear point of view, which is that you are here to do machine learning, not to become a mathematician, and the maths should be taught in service of that goal. Across three courses it covers linear algebra, calculus, and then probability and statistics, and what I appreciate most is that it consistently anchors each idea to where it actually shows up in machine learning, so you are not learning eigenvectors in the abstract, you are learning them because principal component analysis needs them. That framing matters, because motivation is half the battle with mathematics, and a learner who can see why a concept earns its place is far more likely to push through.
It is worth comparing directly to the older Imperial College specialization, which covers similar ground, because the two are the obvious choices and they have different personalities. The Imperial course is a touch more formal and mathematical in flavour, while this one is gentler, more intuition-led, and more explicitly tied to the practitioner's path, and it also gives probability and statistics their proper due rather than treating them as an afterthought. For most people moving toward applied machine learning, I think this is the friendlier and more directly useful of the two. The honest limits are the obvious ones.
It is not rigorous in the way a mathematician would want, there are few real proofs and the depth is deliberately capped, so if you want true mathematical maturity you should take a proper university sequence instead. It still requires real work, because no course can make linear algebra effortless, and anyone with a strong quantitative background will find stretches of it slow. But judged as what it is, a targeted, applied bridge for people who keep getting stopped by the maths in machine learning, it is one of the best things available, and it is the one I would point most self-taught practitioners toward first.