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.

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.

Content and programming assignments are not much correlated. Lots of kernel problems while submitting assignments and late reply by staff.

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.

Not recommend.

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!

Assignments are atrocious, replete with errors. Staff seems not to care as these have been pointed out and left unfixed for months.

Assignments are poorly designed. Staff is unresponsive. IThe same appened with the first course of the specialization.

Do not attempt this course unless you are extremely experienced in the topic and python already.

The exercise doesn't match the course materials at all.

Excellent course.

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.

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.

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.

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

Many technical issues with assignments. Additionally, assignment instructions are often poor or insufficient.

Content is good but assignments are buggy.

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.

Not enough support from the staff. The assignments are strange, some don't relate to the lectures, some are hard to identify what kind of answer is being expected from us. Discussion forums are a joke, I've managed to go through because of some good souls like Kurt Woschnagg. Great lectures though, content is very nice, but not enough visualizations of the math presented.

Too many shortcomings and errors assessments. Tests at the end of the videos cut what Igor is saying and they are often about the following video.

The assessments are also very shallow compared to what we are supposed to learn and the 10-people staff is never online and almost never answers any message.

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

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.

Great overview of main ML concepts with examples applicable to Finance. Even though some people might argue, that the videos don't provide a clear guide path to the assignments, I believe the course provides a simple explanation and great book references! Also, I supplemented my study with courses @DataCamp and other open sources - and it was quite beneficial as well. Thank you, Igor Halperin, & a team!

Furthered my understanding of how probabilistic models are connected to Machine Learning models. Very happy with the content in this course.

Great course which covers both theories as well as practical skills in the real implementations in the financial world.

This is a great course, I strongly recommend. However, the assignments take a while to finish.

Excellent course to get ML algorithms for profit maximization approach