Opiniones y comentarios de aprendices correspondientes a Fundamentals of Machine Learning in Finance por parte de New York University Tandon School of Engineering

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.

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.

It's excellent and incomparable course!

Very informative

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.

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.

A great course with deep insight.

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

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.

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..

Add some hints in the notebooks, it was very hard to understand some parts

Good course with relevant topics, but assignments are not clear sometimes, lack of support with them.

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.

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

the course content is okay. but the coding exam really needs improvement.

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

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

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

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.

Content is good but assignments are buggy.

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!

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

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.

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 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