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.
Relatively tougher than previous two courses in the specialization. I'd suggest giving more time and being patient in pursuit of completing this course and understanding the concepts involved.
автор: Thorben S•
I would have liked to be introduced to the topic on a higher level first - and then, step by step, an introduction of the math to solve specific problems in the progress. That would be a perfect approach, especially for data scientists who just want to understand the underlying math for such a widely used technique.
автор: Jia J W•
The last lab session was a bit bizarre. Quality wise, it's not on par with the previous 2 courses, but it's still a good course. There was quite a huge jump from the previous courses. Be patient with yourself when learning. I think the learning outcomes would make your effort worthwhile.
автор: Andrés M•
I believe the course is proper for people that have no prior knowledge in linear algebra whatsoever. I liked how clear it was to introduce concepts, yet I found that if you knew nothing the course is too hard but super easy for the ones that have some knowledge in algebra and calculus.
автор: Mike W•
The quality of this course is comparable to the previous courses in the specialization, but the math and derivations were harder to follow (even accounting for the increased difficulty of this course). The assignments also were very practical and help reinforce the course's content.
автор: Shariq A•
Thank you professor for providing such a valuable course.
Just I wanted to say one thing without hurting anyone, the week 4 on PCA is not very clear. The derivation are not very correlated .A humble request isthat to elaborate the derivation which would further enhance the learning
автор: Shuqin L•
The last course is especially challenging. The instructor could do a better job to explain the concept and calculation etc. The gap between lectures and assignments is way too big. If the course extends to 6 weeks, it may greatly help improve the quality of the course content.
автор: Aarón M C M•
I think this is one of the bests courses that I have taken. I would just recommend to describe more accurately decimal precisions in tests because it has a little challenging to realize that the solutions proposed were not successful enough because of this issue.
автор: Jonathan F•
This course is way harder than the first two. The maths itself is more difficult. The Python parts are a lot more challenging because they require a good understanding of the way Numpy handles vectors and matrices. But the end result is good and it is worthwhile!
автор: JITHIN P J•
Course content is too hard to understand. You need to go through the content at-least 2 -3 times. But its good. Also assignments are bit tricky and you need to do alot of googling which will make you learn more. Thanks Coursera and ICL for this wonderful course
автор: Moreno C•
This was the most rigorous and demanding of the courses of this specialization.
The video lectures were well organized.
The interaction with the Jupyter Notebook was sometimes confusing but perhaps this was due to my limited knowledge of Python.
автор: Stephan S•
Hi, at first thanks for everyone to make this course possible. In contrast of teh first two parts of the specialization, this course is quite challanging. Some real example would make live a lot easier. Nevertheless in my opinion it is worth the effort.
автор: Shri H•
The programming assignments are very poorly designed (along with bugs ) which makes it really frustrating at times. The Course is overall insightful but requires lots of background study and practice. Basics of Python (using numpy module)is essential.
автор: Gaetano F•
I found the course excellent but in the programming assignments is not always clear what should one exactly do. They are also quite confusing, especially the last one on PCA implementation. One wastes so much time trying to figure out the solution.
some of the mathematical derivations got so detailed that i couldn't follow them. it would be great to add checkpoints in to test/validate/discuss progress so that over a long and complex topic, there can be waypoints to ensure understanding.
автор: Ronald T B•
it is very challenging course, of course you will complain at first on how lack the programming explanation is given. However, it just like the ingredients the math for machine learning will not be complete without attempting to this one.
автор: Вернер А И•
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.
автор: Tuan A T•
The PCA exercises should have been broken into smaller exercises so that it makes it easier to solve. Also, there's a numpy complex dtype issue in the last exercise which requires some debugging to figure out the problem.
автор: Kisan T•
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.
автор: SUJITH V•
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.
автор: João M G•
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
автор: Suyog P•
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.
автор: Alina I H•
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!
автор: Divya M•
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.
автор: Camilo J•
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.