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.

Coding assignment is hard for people who are not familiar with numpy. Would appreciate some material at least going over the basis.

The lecture & instructor are great. But the grader in the assignments really needs revisions. It caused lots of unnecessary stress

Please change courese material for PCA. It is very un-understandable and assignments are also very tugh as per what is taught.

Focus a bit more on PCA in week 4, week 1 was not very informative and should be assumed as required knowledge for the course

Careful, step-by-step construction of the PCA algorithm with practical exercises and coding assignments.. Very well done...

The course level is very difficult and I think having four week course is not enough to understand the math behind PCA

That's a great online courses can help people have enough background to break into Machine Learning or Data science

The PCA derivation part requires more elaborate explanation so that we can understand the concept more intuitively.

Overall the hardest of the specialization, a though one but great to make sense of all the maths learned so far.

Programming assignments are a little difficult. Background knowledge of Python is recommended for this course.

Very difficult course, make sure to do the prereq courses first and understand everything from those courses.

The programming assignments were quite challenging. Some part of the course can discuss this aspect as well.

It is a good course with hard programming, but the assignments could be improved. The forum helps a lot.

The derivations of some concepts have been more vague compared to other courses in this specialization.