Mathematically challenging, but satisfying in the end.

great material but explanation are a little bit messy

Thank you for this course , hope to learn more !

Good course, but requires mathematical background

Good but slightly less deeper than the other two

The instructor is great. HW setup sucks though.

Best course for machine learning enthusiast

The last assignment could use improvement.

a really good course but also really hard

Good for intermediates in linear algebra.

Too much non-video lectures (lot to read)

The last course could have been better.

Challenging but in a good way.

this was hard but insightful

Good, short, overview of PCA

Many errors of homework

so challenging for me

Needs more details

Good Course

kinda long

Good

okk

Broadly speaking, this is a good course. However, the feeling is that it should be twice as long and with more videos. There is simply not enough instruction to facilitate clear learning and completion of this course is down to an individual's desire to read around and problem solve.

In particular, the programming assignments - whilst not technically difficult, lack clear articulation of expectation, which is compounded by pythons slightly inconvenient handling of matrices. Writing vectorised code which involves 1 x N or N x 1 matrices and transpositions often results in zero marks; with no clue whether the code is wrong, the student has misunderstood the expectation or python is refusing to recognise a N x 1 matrix. This could br helped by including more discriptions about the data sets and the variables being used, as well as the expectation of the output.

There are a lot of positives about this course, the videos are well made and are clear. Excellent supplementary learning if you're doing undergraduate Linear Algebra or other Machine Learning courses; just a bit too cramped for a standalone course (even with the others in the specialisation being well understood). Perhaps a four course could be added to this specialisation for "The Basics of Python for Machine Learning" where a student covers all the relevant coding knowledge?

This course had a lot of potential but there were a number of inconsistencies, cut/paste comment bugs, that make it more challenging than it needs to be. The comments in the notebook exercises should be triple-checked with the text above to ensure consistency of variables. Far too often these would be mixed up, or the input/output descriptions would be incorrect. Or the unit test would have different dimensions. Lectures often left out steps - e.g. "because of orthonormal basis, we can simplify and remove a bunch of terms" - how exactly? A extra few seconds of explanations would allow students to follow more closely. Notation in lectures is sloppy - sometimes terms would be missing and then the video would quietly cut to a correction. "j's" and "i's" indices were interchanged frequently making the derivations how to follow. Also, this isn't a course on unit testing - some more tests should be included to help students debug individual functions rather than relying on the final algorithm (e.g. PCA to work). It should be explained why the "1/N" term for XX^T is not necessary even though it's in the lectures. On the plus side, the added written notes were welcome and fairly well done.

This course is interesting and taught me basics of PCA. Nonetheless, the last assignment needs revision by the Instructors of this course. There is even an error in one of the assertion tests! And some cells cannot be executed because of some Python syntax or traceback errors. Although these cells are not run by the Autograder, you nontheless cannot see the complete output the notebook is trying to show you. Lastly, in the error messages printe by the Autograder, I cannot seem to find which function is giving me the error! In a previous course by another University with similar programming assignment, the Autograder will point to the erroneous function and shout where went wrong.

I can expect students who have little Python programming to struggle greatly with this course - especially the last assignment. However I believe once the errors are fixed in the notebook, students may have a better experience completing it. I have completed many Coursera courses and I studied Statistics - even I got confused by the last assignment! I believe many students share this sentiment as well.