Very good course. I liked every single video and exercise. I feel that the programming assignments were a bit more challenging and sometimes I was not too sure of what I was doing. I am not a professional in handling Python, so I had to surf online finding the commands to be able to build the simplest code possible. Other than that, it was enjoyable.

The course presents the basics for and concept of PCA in a quite approachable way and additionally provides some really interesting interpretations of projections and PCA. Unfortunately, the programming labs could use some additional hints for people unfamiliar with Python and Numpy without which some small errors can lead to great frustration.

I enjoyed this course. Maybe it's the one I enyoyed the most. When completing something a bit difficult you can feel satisfied.

I liked the teacher and his pace.

That being said, from reading reviews I feel some are right, the information given is sometimes lacking and a couple of times just lazy (some readings were just links to wikipedia).

good thing is it's trying to give you a sense of practically how to do it.downside is it's not really bridging to from maths to that practical sense in python (and the online jupyter notebook is terrible).the teaching staff is actually more responsive than the other 2 in the specialization.a bit more sided on python than maths though.

This maybe the most frustrating course and most advance compare to 2 other courses, you might confuse about the code in the assignment of this course. So, if you do have basic background about coding with numpy, matrices,etc..., I do recommend this course, if you qualify enough to fix the bugs of what the dev team left.

This is a wonderful course in general, and I like the video and text course material, especially the interactive quiz and lab.

Yet there is somewhere to improve, such as make it easier for non-professionals to better understanding, e.g. more steps by step visualized explanations to reduce difficulty in comprehension.

I would have liked to be introduced to the topic on a higher level first - and then, step by step, an introduction of the math to solve specific problems in the progress. That would be a perfect approach, especially for data scientists who just want to understand the underlying math for such a widely used technique.

The last lab session was a bit bizarre. Quality wise, it's not on par with the previous 2 courses, but it's still a good course. There was quite a huge jump from the previous courses. Be patient with yourself when learning. I think the learning outcomes would make your effort worthwhile.

I believe the course is proper for people that have no prior knowledge in linear algebra whatsoever. I liked how clear it was to introduce concepts, yet I found that if you knew nothing the course is too hard but super easy for the ones that have some knowledge in algebra and calculus.

This course was a challenging and enjoyable journey and it put into practice the information from the previous courses (Linear Algebra and Multivariate Calculus). However, there were some issues with grading, which has been reported on the forums months ago and haven't been fixed yet.

The quality of this course is comparable to the previous courses in the specialization, but the math and derivations were harder to follow (even accounting for the increased difficulty of this course). The assignments also were very practical and help reinforce the course's content.

Thank you professor for providing such a valuable course.

Just I wanted to say one thing without hurting anyone, the week 4 on PCA is not very clear. The derivation are not very correlated .A humble request isthat to elaborate the derivation which would further enhance the learning

The last course is especially challenging. The instructor could do a better job to explain the concept and calculation etc. The gap between lectures and assignments is way too big. If the course extends to 6 weeks, it may greatly help improve the quality of the course content.

I think this is one of the bests courses that I have taken. I would just recommend to describe more accurately decimal precisions in tests because it has a little challenging to realize that the solutions proposed were not successful enough because of this issue.

This course is way harder than the first two. The maths itself is more difficult. The Python parts are a lot more challenging because they require a good understanding of the way Numpy handles vectors and matrices. But the end result is good and it is worthwhile!

Course content is too hard to understand. You need to go through the content at-least 2 -3 times. But its good. Also assignments are bit tricky and you need to do alot of googling which will make you learn more. Thanks Coursera and ICL for this wonderful course

This was the most rigorous and demanding of the courses of this specialization.

The video lectures were well organized.

The interaction with the Jupyter Notebook was sometimes confusing but perhaps this was due to my limited knowledge of Python.

Thank you.

Hi, at first thanks for everyone to make this course possible. In contrast of teh first two parts of the specialization, this course is quite challanging. Some real example would make live a lot easier. Nevertheless in my opinion it is worth the effort.

The programming assignments are very poorly designed (along with bugs ) which makes it really frustrating at times. The Course is overall insightful but requires lots of background study and practice. Basics of Python (using numpy module)is essential.

The first two courses of the "Mathematics for Machine Learning" specialization were definitly better organised and easier to understand. I did not recognize any support from Coursera or Imperial College London to help learners with their questions.

I found the course excellent but in the programming assignments is not always clear what should one exactly do. They are also quite confusing, especially the last one on PCA implementation. One wastes so much time trying to figure out the solution.

some of the mathematical derivations got so detailed that i couldn't follow them. it would be great to add checkpoints in to test/validate/discuss progress so that over a long and complex topic, there can be waypoints to ensure understanding.

it is very challenging course, of course you will complain at first on how lack the programming explanation is given. However, it just like the ingredients the math for machine learning will not be complete without attempting to this one.

Very tough course because of the programming assignments. Material was sometimes taught in a non-clear and deceiving way, e.g. covariance matrix of a dataset. Nevertheless, the course is good and covers lots of important details.

The PCA exercises should have been broken into smaller exercises so that it makes it easier to solve. Also, there's a numpy complex dtype issue in the last exercise which requires some debugging to figure out the problem.