Very helpful to review and get introduced to mathematical concepts behind machine learning. There is a fair bit of practical exercises as well. The only thing I am less happy about this cousre was a lack of additional suporting materials and references to other resources to help gain more knowledge on the subject.

I had no previous experience with multivariate calculus. This was a nice introduction to the topic, but in my opinion it does not allow me to say that "I know" multivariate calculus. Nevertheless, I think it is work taking as an introduction before going to more complete courses in multivariate calculus.

I think Samuel Cooper is an amazing instructor. However, the last two weeks taught by David Dye were very difficult to follow. I think David should improve his explanations because I did not enjoy too much his course on linear algebra, and this course was great until he started with the last two weeks.

Very solid introduction into Calculus. Keep in mind that this is a course meant to give you an intuition and basic understanding. Sometimes there are small gaps in the curriculum to the quiz (but you will easily be able to make up for them by just reading the according Wikipedia page). Was a pleasure.

exercise and programming assignments are good ....and i can grow a sound concepts after completeing them.lectures are also good ...but some lecatures are too quick and a little elaboratiion in some places would have been helpful(particularly those in the last couple of lectures)

Excellent course. It covers so much without making me feel overwhelmed. I would like to see more hands-on demonstration on linear and non-linear regression, but I was able to complete the quizzes and assignments. This without any previous multivariate calculus instruction.

Overall a good course to give us a better idea of what sort of math is used in ML. But I feel they went too fast in this course, so I personally lagged a bit in understanding certain crucial concepts. Also, it'd be much help if the instructors could mention reference books.

I would say this entire series is better advertised as a quick *review* of the pertinent concepts. Otherwise, someone with no background in the topics covered may struggle (unless they are particularly talented with quickly learning new mathematical concepts).

This course is good to start learning Machine Learning. There are also labs practice so that I can acquire deeper understanding by the visualization. However, some materials are not explained clearly, such as Newton-Raphson method and Lagrange Multiplier.

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

This was a good course. The instructors kept me interested in a rather dry subject. The exercises were well designed. There are courses on Coursera which are not worth the time. This course is not one of those.

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.

All was great until weeks 5 and 6. For these weeks, there weren't enough explanations in order to really understand the subject. Some very important steps were simplified, which made understanding difficult.

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 .