Mathematics for Machine Learning: Multivariate Calculus

A nice course on multivariate calculus.

This course is really informative and builds intuition for the topics covered, I'd like to specially thank Sam for his amazing way of teaching and his visualizations :)

Fantastic courses

This course is perfect for those who prefer to understand the intuition behind multivariate calculus, visualize the power of gradients in optimizing functions, and apply calculus to machine learning with robust understanding of underlying mathematical concepts.

Excelent course

Brilliant course that covers so much in such a short time span.

Relevant content. Great instructions. Likable instructors. Very bad coding assignments.

Very valuable training course from the insight/intuition point of view. This is more of an overview of the calculus for machine learning giving the student a good direction of what to study and where to start from. I think that actually mastering the subject will require extensive additional exercises from other sources

Great course, I really enjoyed it. The way of teaching visually rather than theoretically keeps those that get lost in formulae like me on track. The visual explanation of what a derivative is helps a lot for instance.

Fun and challenging course! It's priceless to learn all the math behind neural networks and other machine learning algorithms without having to learn all of calculus and all of linear algebra. Those are large fields and having the material presented in a way that focuses on the most relevant pieces is hugely valuable.

I am not sure if this course is for people who know calc, and want to learn python coding or people who know coding and want to learn some calc? Frankly it isn't a great course for either group. I knew some calc, and some python going in, and I did expand my knowledge and gain some useful practice. But it left me craving rigor.

I don't want to be too hard on this course since I really
liked some parts of it. Especially, the instructor in Week 1 - 4 did a good job
explaining the concepts and overall one can clearly see that a lot of effort
was put into the creation of this course. However, I found that a lot of topics
could be handled a lot more in-depth. The assessment at the end of a week was not
really challenging and does not require a deep understanding of the concepts.
Some of the quizzes were more challenging but in the assessments it was often
only required to answer questions based on graphs or other images of
functions. Most of the programming assignments only required the student
to fill in some easier blanks.I still do not know what the Taylor
Series Chapter was about. I guess this is an important concept but I was
not sure how this relates to machine learning. If you call a course Math
for Machine Learning, I would expect that you relate the concepts to Machine
Learning.Maybe, it
is just me but I would have been glad if this course had offered more depth and
took at least double the amount of time to complete. This would have been more
rewarding, as I do not feel that I learned as much as I hoped for when I
started this course.