Ritvik Kharkar's channel, ritvikmath, occupies a specific and genuinely valuable niche, the mathematics and statistics that sit underneath data science and machine learning and that so many courses skate over on the way to the code. A lot of learners hit the same wall, they can import a library and fit a model but they do not really understand the probability, the distributions, the inference or the assumptions underneath, and this is the channel that tends to get them over it. Ritvik works in short, single topic videos, usually with a whiteboard or a tablet, and his strength is intuition, so instead of putting a formula on screen and moving on he builds up why the formula looks the way it does and what it is really doing. That makes topics that are often taught badly, Bayesian inference, maximum likelihood, the bias variance trade off, time series models, hypothesis testing, land in a way they frequently do not elsewhere, and he covers some corners, particularly Bayesian methods and time series, that are noticeably underserved by the big video courses.
Because each video is self contained and clearly titled, the channel doubles as a kind of searchable reference, so when a specific idea in your main course will not click you can very often find a ten minute explanation here that fixes it. The trade offs are the natural consequences of the format. This is a library rather than a curriculum, so there is no built in order and no sense of progression, and you are responsible for knowing what you need and stitching the pieces together yourself. It is also deliberately light on code, so it teaches you to understand methods far more than to implement them, which means it pairs with a hands on course rather than replacing one.
The same short format that makes it so approachable caps how deep any single video can go, so once you are past intuition and into genuinely advanced treatment you will need to move to books or longer courses. And a complete beginner who does not yet know which concept is missing will get less from it than someone who can already point at their specific gap. My take is that ritvikmath is one of the best free resources going for the mathematical underpinnings of machine learning, as long as you use it as a supplement. Learn from a structured course, do the coding somewhere else, and treat this channel as the place you go the moment the statistics stop making sense, because more often than not Ritvik will explain the thing that was blocking you better and faster than the course that introduced it.