Great Course but not good as previous two courses. It helps me gather great idea about Principle Component Analysis. Thanks to Coursera, Imperial College London, and Professors for this amazing course and specialization.

This is a great course. It covers the topic in good amount of detail. I have enjoyed this course a lot and it also made me think deeper at a lot of places. I am motivated to go and do more work on related topics now.

The course was great till the final week. The lectures did not explain very well the concepts and the assignment was poorly designed. It's a shame because I've loved the more rigorous way of this final course.

I think it's really a hard lesson for me, but I've also learn a lot, thanks a lot for the teacher and coursera. Some Programming test take too long to execute, and there are some errors in it. just be careful

Finally understood basic intuition of PCA, never got perfect resource before. However, there was a sharp change in terms of course delivery than the previous two courses of this specialization. So, heads up.

Sometimes the instructions in the labs were a little unclear. Also, the instructor could have displayed a little more fun - but I guess that's how we Germans are ;) still a very recommendable course!

The Programming assignments are quite challenging. The teaching part doesn't equip you with enough resources regarding numpy to get full marks in the Programming Assignments. Good teaching though.

Great capstone for the three-class Mathematics for Machine Learning series. Assignments were way harder and programming debugging skills had to be appropiate in order to finish the class.

Very challenging at times, but very good course none the less. Would recommend to any one who has a solid foundation of Linear Algebra (Course 1) and Multivariate Calculus (Course 2).

Fascinating course! The lecturer gives very detailed illustrations to many complicate concepts. It will be much better if the submitting systems work fine for the last assignment.

Course content is interesting and well planned, Can be improved by making it Simpler for Students as it was more technical than the other 2 courses of the Specialization.

it's very fantastic course.i enjoyed a lot.i feel reading material should be increases in those courses,others things are perfectly ok.thanks for offering this courses.

Course content tackles a difficult topic well. Only flaw is that programming assignments are poorly designed in some places and are quite difficult to pick up at times.

The course is generally good but the assignment setting definitely needs to be rectified. Thanks anyway for this course. An important element of machine learning.

Teaching pacing is good, and clear in explanation. It will be good if there are some examples about how we should apply all these theories to some real problems.

Overall the course was great. The only thing was that there was a lot I didn't understand from the videos. The recommended textbook resource was a great help.

The other two courses were much more didactic. And there were some bugs in these courses assignments... But, overall, it was a great course on the subject

I learned a lot in this course, though the last week was somehow hurried and the lecturer didn't spend enough time to piece the whole stuff together.

The explanation of the model is very precise but there are unnecesary comments for PCA omit the comments related with std in the final assignment

A different course than the previous 2.

Much harder - as you have to actually know some Python tricks.

But I guess it's the same in a real world.

Course addresses important subject, but I worth like to have more in-depth explanation of the mathematics by the instructors and more examples.

Felt like explanations in this course were a bit confusing, but otherwise, it was a very interesting course. Thank you so much for doing this.

The final Notebook contains some errors (Xbar instead of X, passed as an argument). Otherwise a very well organized course. Thanks a lot!

This was a very hard course for me. But I think the instructor has done the best possible he can with presenting and explaining the course

Jupyter notebook assignments are in desperate need of attention! Very buggy or non-intuitive for the scope of material in span of time.