Sep 08, 2018
This Prof. really have the talent of complicating even the most simplest of the ideas. His teaching method is really bad. Plus some assignments have nothing to do with that week's lectures.
By Sean H•
Jul 31, 2018
The material is promising, but the staff running the course do not give a lot of direction on how to pursue learning the content. On the other hand, there is a lot of repeated material from the previous course. I do not know if they expect students to jump in at different parts of the specialization, but it seemed unnecessary. The rest of this review is a repeat of my review for the previous course, but still holds true in this course. The programming assignments are left almost completely to the students guessing what they're suppose to do with little direction. There is almost no feedback on how your code has performed, except to say that your code was wrong, which you already understand from not getting the points. While I was able to achieve a passing grade in this course, it was only because of the community of students that figured things out together, but with no other reliable way of figuring the material out. The code was also rife with bugs that weren't fixed for weeks while students tried and failed over and over again to pass assignments that they simply could not pass. It ended up wasting many hours of my time and, no doubt, other students' time. Simply check the forums to see the frustration from the Coursera community, that normally expects and receives high quality educational content.
Nov 06, 2018
It's excellent and incomparable course!
By Pavel K•
Nov 28, 2018
By Luis A A C•
Jan 07, 2019
I only wish to have had programming assignment with RNN and Hidden Markov Models instead of three assignments on PCA. Although they highlighted a interesting application in finance.
By Zoltan S•
Aug 11, 2018
The lectures were truly outstanding, the best overview on different methods in machine learning I have seen so far. The problem sets were also interesting, informative and introduced several useful api from sklearn, tensorflow. With a little work these problem sets could (and probably should) be improved to match the quality of the lectures. For example adding more clarifications in the homework notebooks would be very helpful. Having said this, I think this is an excellent course, and highly recommend it.
By Yuning C•
Sep 08, 2018
A great course with deep insight.
By Angelo J I T•
Aug 10, 2019
Furthered my understanding of how probabilistic models are connected to Machine Learning models. Very happy with the content in this course.
By Jacques J•
Dec 25, 2018
So far so good. The lecturer refers to projects of which some weren't covered in this course. So a little confusing. Takes lots of googling to finish this course.
By Hilmi E•
Aug 05, 2018
Good material..The course would improve a lot if there were clear explanations for the goals of the assignments and the plan for the assignment.. The codes for the assignment should be fully debugged..
By Bozanian K•
Aug 19, 2018
Add some hints in the notebooks, it was very hard to understand some parts
By Aydar A•
Jun 28, 2019
Good course with relevant topics, but assignments are not clear sometimes, lack of support with them.
By Umendra C•
Feb 02, 2019
This could have been the real deal with so many fascinating topics to learn here, but unfortunately, this specialization is setting new low standards in each assignments. The grader does not work, sometime we are asked to produce wrong results (as oppose to the research material). It is very frustrating!
Good reading assignments.
They need better and more qualified support staff.
By Pramanshu R•
Jan 08, 2019
Content and programming assignments are not much correlated. Lots of kernel problems while submitting assignments and late reply by staff.
Feb 24, 2019
the course content is okay. but the coding exam really needs improvement.
By cyril c•
Oct 11, 2018
content of the lessons is quite good, I would give it 5 stars if the assignments weren't so buggy, contains mistakes, unclear instructions, no help from staff/moderator/instructor, technical issues that are not resolved, etc. a lot of frustration, it just feels like the course was rushed to production and they let the students debug it
By Philip T•
Oct 25, 2018
Many technical issues with assignments. Additionally, assignment instructions are often poor or insufficient.
By Amalka W•
Nov 01, 2018
If assignment are clear this course would be a great one. So I would like to suggest that explain more details about assignment and some guide lines
By Nicolas M•
Apr 01, 2019
good overview of methods but project part was frustrating due to slow Jupyter servers which blocked progress. Overall still positive as course content is unique.
By Daniel F•
Jan 13, 2019
Content is good but assignments are buggy.
By Andreas A•
Nov 21, 2018
Completely horrible labs.
And no response on the forums, errors in the labs remains for several months.
This is not acceptable, the course should be removed from Coursera!
By Teemu P•
Mar 03, 2019
Do not attempt this course unless you are extremely experienced in the topic and python already.
By Minglu Z•
Aug 06, 2018
The assignment submitting problem is fixed. But the confusing requirements are still in assignments. Always be stuck by concept or formula which irrelevant to the ML.
By tze s•
Sep 02, 2018
WORST CLASS EVER. Stay away!!!! I want my money back. (and even that is not possible).
the homework autograder does not work. The mentors tell you to simply upload code of which everybody knows that it is incorrect instead of fixing the autograder.
Sometimes those incorrect "fixes" that the mentors give, don't work either. So no way of finishing the class.
Audio of the videos is of very poor quality.
By Pierre C D M•
Oct 14, 2018
Not Worth the money. Although the assignments is a bit better than in the first course of the specialization, there is no help at all from the coursera team, even when it is impossible to grade the assignment. Do not spend your money there and buy some book instead