Learner Reviews & Feedback for Mathematics for Machine Learning: Multivariate Calculus by Imperial College London

This felt like time well spent. A really good course which I should have taken before doing the Machine Learning Course by Andrew Ng. That would have made life easier.

Beware, the 'gradient of the learning curve' at any point during this course is steep.

Generally, it is a good course. Many new tools and fancy representation method, but for mathematical idea and explanation, it is just too simple. Maybe the biggest contribution to me is that It lets me know the Kahn Academy and 3b1b courses.

Could have explained in a way that the audience requires a slower and better and little more in-depth explanation. Some places felt a little rushed, so had to spend more time in forums and other resources to get more idea. Overall was great.

This course assumes some knowledge of Python. Some topics are taught quite quickly, and overall this is not a comprehensive course. A good introduction to multivariate calculus. I suggest learners use Khan's Academy in supplement to this.

Need a bit more clarity in terms of integrating the calculus in the last week sessions.

I agree they are very good but would be great if there is some more additional clarity. And also some project using the whole course would be helpful.

It gives you a good head-start to the math required in Machine learning. Some major concepts are touched just on the surface level but the mathematics involved in those concepts is explained quite well. Overall, it's good experience

Very good course to start of with mutivariable calculus basics. Helps to refresh your memory if already familiar with concepts, additionally helps in getting fresher perspective because of geometrical intuition presented very well.

Good details, great at building intuitions. Instructors are pleasant to listen to :)

As expected, it's enough to get you going in the right direction, that if you want to know more, you'd have enough knowledge to build on from.

Lack of support from the staff. Some parts/lectures are not clearly explained (for example, constrained optimization) and some quiz questions are not directly related to the course content. Otherwise, it's a very good course.

up to week 5 , it was masterpiece.

week6 (although it should be the most important one) was a mess and disappointing.. as it was not explainable, i couldn't link what is happening with previous weeks.. require to be enhanced

The first 3 weeks is really good, the fourth week is okay but the last 2 weeks is kinda confusing. The explanation is quite clear but it is quite hard to grasp the intuition and relationship between each material.

Along with the advanced and popular technique, this course gives me impressive insight over how machine learning works. But it would be much better if the concept in linear algebra combines more with this course.

I thoroughly enjoyed this course. The materials were good and the course content was good enough to pass all the assignments and quizzes. This is way better than the linear algebra course in this specialization.

i think some of concepts touched the surface and it was difficult to get a deep understanding .Probably the course could have provided some external links for those topics where people could read .

Superb quality. The way instructors teach is really innovative. The course is good in terms of the area it covers but lacks depth, but is a good starting point if you want to dwell more in detail.

Very informative refresher on the basics of differentiation, though some of the later topics could have been fleshed out more (i.e. Taylor Series, Lagrange Multipliers, etc). Overall very good.

I wish, Linear Regression was taught with a little more clarity. Seemed like too many things were happening. Otherwise, a very good course. Really enjoyed the back-propagation week.

the basic concepts are explained clearly, but the step of the lecture became more fast than the course of linear algebra. More detail proof and application of theory is expected.

T​he course starts off somewhat too basic for my taste but still gives a great intuition for some off the fundamental concepts underlying gradient descent and machine learning.

The course is still a bit young, some errors appear here and there sometimes, and some parts of it are a bit steep.

Otherwise, this is a good course, focused on derivatives.

They should explain concepts in detail. They just explained really tough concepts in 5 min and gave much much harder assignments which was really frustrating at many times.

Practice makes about 65% of this course, 3Blue!Brown and Khan Academy are indispensable in this course too. The course is more like an overview than an in-depth study.

1.Week 5 should be taken in separate module dedicated to statistics.

2.The duration of course can be increased.

3. Week 3 and week 4 can be made more detailed

Very good course but especially while approaching the end of the course some steps are being skipped or not explained entirely which can be a bit confusing

This course is a good refresher, bringing all the important aspects and how it can be applied for machine learning also combining with linear algebra