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!
par Liang S•
Jul 09, 2018
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.
par Mark S•
Jul 07, 2018
Loved the course, although I wish there was more ramp up to some of the complex scenarios (or anything simple but new). Very helpful forums/community. Requires a fair amount of external reading/referencing for some of the concepts which seem to be covered only at a high level in the lectures.I would love to see more courses on applied mathematics for machine learning.
par Вернер А И•
Mar 18, 2018
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.
par Evgeny ( C•
Jul 25, 2018
It was a harder course where I spent double the time I have initially anticipated.
It is much harder than the two predecessor courses in specialization, and amount of direction when it comes to doing exercises is significantly smaller. More Python knowledge is required.
That said, I feel like I have finally understood the PCA and math behind it, which made it all worth it
par Jerome M•
Jul 26, 2018
The best of the 3 courses. This is a refresh course of course. A solid background in linear algebra is required in order to fully understand everything. I personnaly recommen the MIT course from Gilbert Strang before you try this one. The python exercises are very well designed and I can only be thankful to having shared this knowledge. Thank you Imperial College.
Jul 30, 2018
still have room for improvement since lots of stuffs can be discussed more in detail.
par Nelson F A•
Apr 25, 2019
This course brings together many of the concepts from the first two courses of the specialization. If you worked through them already, then this course is a must. There are some issues with the programming assignments and the lectures could do with some more practical examples. Be sure to check the discussions forums for help. For me they were essential to passing the course.
par Andrew D•
Jun 02, 2019
Very difficult course, make sure to do the prereq courses first and understand everything from those courses.
par Eddery L•
May 24, 2019
The instructor is great. HW setup sucks though.
par Giri G•
Jun 07, 2019
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
par Phạm N M H•
Jul 12, 2019
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.
par Jiaxuan L•
Jul 15, 2019
Overall a good course. Very limited introduction to Python though.
par Kwak T h•
Jul 27, 2019
Good but slightly less deeper than the other two
par Nikolay B•
Aug 03, 2019
Instructor gives the very dry but useful essence of the "philosophical" concepts of dot and generalized inner product, etc., - personally, liked that. Unfortunately, the offered problems are so far away from the delivered videos but the web search helps on getting the hints. This course makes you think - I learned a lot just by asking myself "what do they mean under this statement?", what they want in this task? Though I will appreciate if providers elaborate the material further and so instead of googling we spend our time watching - a single point access.
par Jessica P•
Aug 06, 2019
I agree with the others. Course didn't merge well with the 1st two which were perfect!
par Berkay E•
Aug 09, 2019
-Some of the contents are not clear.
+It gets great intuition for new learners in machine learning.
par João M G•
Aug 14, 2019
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.
par Jordan V•
Aug 23, 2019
Course addresses important subject, but I worth like to have more in-depth explanation of the mathematics by the instructors and more examples.
Aug 29, 2019
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
par Suyog P•
Sep 02, 2019
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.
par Xin W•
Sep 06, 2019
This course is full of mathematical derivation, so it is kind of boring.
par Abhishek P•
Sep 09, 2019
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.
par k v k•
Nov 30, 2018
its a good course to learn mathematics essential for machine learning
par Prashant D•
Feb 17, 2019
The lecturer is good and probably has a very good understanding of the mathematics. However if you are looking for a light and easy course, then this one is not for you. The mathematics is sometimes difficult to follow and although the lecturer patiently explains the derivation of the results, I had to go back and forth a number of times to understand what was happening.
par Manju S•
Jan 29, 2019
Instructor has good knowledge of the subject. The course content structure is designed well.
Concepts could have been presented with more clarity. Programming assignments need more instructions and less assumption on what the students already know.