6 июля 2021 г.
Now i feel confident about pursuing machine learning courses in the future as I have learned most of the mathematics which will be helpful in building the base for machine learning, data science.
16 июля 2018 г.
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.
автор: Andrés M•
4 июля 2020 г.
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•
22 мар. 2020 г.
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•
20 окт. 2019 г.
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•
4 авг. 2020 г.
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•
10 мая 2020 г.
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•
17 мар. 2019 г.
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•
27 апр. 2020 г.
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•
14 мар. 2020 г.
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•
6 мар. 2020 г.
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•
22 авг. 2020 г.
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: Stephan P•
18 янв. 2023 г.
The first two courses of the "Mathematics for Machine Learning" specialization were definitly better organised and easier to understand. I did not recognize any support from Coursera or Imperial College London to help learners with their questions.
автор: Gaetano F•
10 окт. 2019 г.
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.
2 мая 2020 г.
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 B•
21 янв. 2019 г.
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•
17 мар. 2018 г.
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•
8 мар. 2021 г.
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•
16 июня 2020 г.
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.
27 сент. 2018 г.
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•
14 авг. 2019 г.
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.
29 авг. 2019 г.
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•
2 сент. 2019 г.
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•
19 янв. 2021 г.
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•
17 нояб. 2020 г.
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•
1 мар. 2019 г.
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.
автор: Lotachukwu I•
27 июня 2020 г.
Very challenging at times, but very good course none the less. Would recommend to any one who has a solid foundation of Linear Algebra (Course 1) and Multivariate Calculus (Course 2).