Jul 17, 2018
This is one hell of an inspiring course that demystified the difficult concepts and math behind PCA. Excellent instructors in imparting the these knowledge with easy-to-understand illustrations.
May 01, 2018
This course was definitely a bit more complex, not so much in assignments but in the core concepts handled, than the others in the specialisation. Overall, it was fun to do this course!
By Xin W•
Sep 06, 2019
This course is full of mathematical derivation, so it is kind of boring.
By Jiaxuan L•
Jul 15, 2019
Overall a good course. Very limited introduction to Python though.
Feb 04, 2019
By Ashok B B•
Feb 06, 2020
Course was challenging , but learned the maths behind PCA,
By Cesar A P C J•
Dec 23, 2018
Good content, just need to fix the assignments' platform.
By ADITYA K•
May 13, 2020
It is very informative and hands-on based Course for PCA
By Md. S B S•
May 04, 2020
Not as good as the other two courses..but interesting!
By Sharon P•
Sep 25, 2018
Mathematically challenging, but satisfying in the end.
By Paulo Y C•
Feb 11, 2019
great material but explanation are a little bit messy
By Kwak T h•
Jul 27, 2019
Good but slightly less deeper than the other two
By Eddery L•
May 24, 2019
The instructor is great. HW setup sucks though.
By Manish C•
May 06, 2020
Best course for machine learning enthusiast
By Romesh M P•
Jan 16, 2020
Too much non-video lectures (lot to read)
By Mark R•
Jan 22, 2019
Good, short, overview of PCA
By Changxin W•
Jan 28, 2019
Many errors of homework
By Sammy R•
Dec 25, 2019
Needs more details
By Harrison B•
Apr 18, 2020
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?
By Mark P•
Jul 30, 2019
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.
By Yuchi C•
Feb 23, 2020
The lectures (especially the last module) are fast-paced and intense, they're informative and very interesting to do. To complete and fully understand the course contents, heavy self-research is apparently required for students with no to no foundation.
I do not believe that the programming assignments match well with the lectures, they're more about programming than testing knowledge. Compared to the assignments in the first two courses from the same specialisation, the assignments in this course are very difficult for students with little to no coding experience. I highly advise explanation and solutions to the assignments to be published after completion (if possible) so that students get to know where they went wrong.
Overall, I enjoyed the course despite having spent too much time on the programming assignments, trying to spot my mistakes without any direction and dealing with the unstable programming environment.
By Philippe R•
May 16, 2018
Very mixed feelings about this course. First three weeks are OK, but going from week 3 to week 4 is like a HUGE step in difficulty if you really want to follow it all. Which is a pity because week 4 is the whole purpose for the course!
I learned "some" about the subject, but not to the level that I can say I understand it fully.
The assignments are OK, but the instructions are not always all that clear, leaving you at times wondering what is expected from you. And not that it is specific to this course, but the grader feedback is not all that helpful. If that is the only information you rely on to figure out where you may have gone wrong in a programming assignment, fixing your mistakes is likely to take quite some time.
All in all, an "OK" course, but not one that I would take again. I will most likely resort to other sources to get a better understanding of the subject.
By Piotr G•
Apr 23, 2018
This course is overall good in terms of the accuracy and obvious deep knowledge of the tutor. However, after the first two modules of this course I expected a completely different approach with way more conceptual thinking than writing proofs and long derivations which can be found on Wikipedia and other websites. It seems to me that there is a clear mismatch between the styles of the first 2 modules and the 3rd course. I'm giving it only three stars because this is not what I expected, I signed up for this track to gain additional conceptual overview of how maths in many machine learning applications works on high level. On the other side though, the assignments and quizzes were harder in this course which is a big plus.
By Michael P G•
May 22, 2020
The mathematical skills needed to take this class exceed what was covered in the previous two classes of this specialization. The instructor provided reading materials that were just wikipedia pages, not designed to teach a newcomer.
The programming exercises were poorly designed. In some cases it was not at all clear what one is supposed to implement, what the shape of the input numpy arrays are, etc. In one case, the automated graded system provides inputs that are unlike the provided example inputs, so one has no idea what to be coding for.
I took all three courses in this specialization: the first two got 5 stars, this one gets only 3.
By Ben H•
Aug 20, 2019
This course had a lot of potential, but unfortunately the pacing, structure, and teaching was not up to the standard of the other two courses in the specialisation. The teacher is clearly very knowledgable about his subject, and seems like a really nice person, but delivers the material in a very direct, formal mathematical style. This makes it much more difficult to gain intuitive insight into the subject matter.
Given the level of the past two courses, this felt like way too big a leap. Don't get me wrong — this course is still worthwhile, but could use some refining.
By Christopher R•
Apr 13, 2020
The first two courses in this series were amazing and provided a very intuitive understanding of the mathematics. I felt like I had no idea what we i was actually trying to do this entire course and basically had to learn it all on my own. This was basically a punch in the face and required much more background knowledge of linear algebra/python than the previous classes provided. If you are going into this third course of MML I recommend you do some outside study beforehand to get up to speed or else you might be spinning your wheels and get frustrated.
By Nont N•
Sep 25, 2019
I am a bit disappointed by this course. The professor didn't do much to help learner understand what's the meaning of the math we are looking at. Much of the quiz is just math grinding. The programming assignment require a lot of my effort in programming, but not much on math.
I'm not saying that this course is very bad, but Compare to the previous 2 course in the Math for ML specialization, provided by the same university, this one is obviously inferior.