good content

Great Course

Thank you!

Good overview of ML in Finance, clearly based on real-world experience. Would not recommend this as a first ML course; probably more useful after first taking another more general course, such as Guestrin's UW ML specialization. Some of the quizzes and exercises seem a bit rushed; e.g., out of order vs. the lectures and not clear about what is required. It was sometimes necessary to consult the discussion forums for clarification. The most useful part may be the categorization of ML algorithms along different axes, including applicability to different areas of finance. The readings and coding exercises seem to come mostly from Geron's O'Reilly book, so plan on buying that (it's a great book, so you should buy it whether you take this course or not).

This course has been informative, and extremely FUN! This is not to say that it's perfect, in fact as others say the assignments are quite challenging because there's little introduction to the problem/solution being asked. But that's exactly where the fun is! You need to search for the information yourself to solve the problem, much like in the real world. In fact I took another course on TensorFlow in the middle of this course to finish the assignment. But I can imagine this would be frustrating for those with less background on ML or programming, or people who expect everything to be presented smoothly for them.

A much needed course by a very seasoned expert in the field, bringing the right blend of backgrounds in finance and tech. The course is well designed for finance professionals with some coding background and for technology professionals with some finance background - which is unique in that sense. Some bridging between lectures and assignments is needed but that kind of fine tuning is inevitable and as more students enroll, the discussion rooms and feedback will provide that sharpening at the edges organically. All in all, I enjoyed the course a lot and look forward to the next three in the specialization!

Lectures are very good and the use of financial examples really brings the subject alive. However the final projects are not very closely linked to the material taught, it's possible to pass if you ignore the new material. It would also be nice to update the tensorflow code from 1.0 to 2.0 as it would make things much easier to debug.

Potentially great course with bridges technology (machine learning methods) and application (finance), but as for now it is really rough around the edges. Still needs to improve in terms of video lectures, resources and assignments; but once polished it could be a great course/specialization.

Um curso um que demanda um pouco mais que o usual, partindo desde o princípio de um ritmo rápido, com tarefas contendo explicações de somente o estritamente necessário. Entretanto, com uma temática muito interessante, e utilizando de várias técnicas.

Assignments are extremely difficult because the instructions are not clear. I understand that the act of working through the assignments is how you learn the material, however, this goes beyond that. It felt like a battle.

Great lectures. Homework is not trivial: it requires web searches and significantly more perseverance than, say, Andrew Ng's courses. Only 4 stars because I didn't see any recent signs of active support in the Forums.

The course seems a bit of date (tensorflow) and 'lazy' -- assignments are sloppy, not related to the content of lectures sometimes, sometimes just replay of things form reading material... Promised more than delivered.

Lectures are mostly short review of the topic. So you should know topics beforehand or supplement it with readings. Problems are great, you cannot solve it unless you understand the concept properly, so that good point.

Very interesting course. Covers the main algorithms of supervised machine learning and their applications to the world of finance. The one and only down is that programming session are a little hard to understand

Deep introduction into machine learning in finance. A bit outdated API-usage (Tensorflow 1), but nevertheless a great introduction for those who want to understand how the NN are processing the data.

Despite all the problems with the assignments and the grader this course provides really good overview ML tools and their application to finance. It's definitely worth the effort

The course content is a mix of theory and practical stuff. One star off is due to the poor quality of programming assignment, i.e., unclear instructions and explanations.

Good material but assignments explanation were too sparse and even expectation of material not covered in videos or readings (example is Tobit regression in week 4).

To much math in lectures, assignments are not coherent and complicated, im not sure that i need tensorflow from scratch to work with finance(Keras fits better)

Great general overview of machine learning. I think the course can be re-organized to incorporate some of the theory and some coding tips as well, however.

The course is great, but the code assessment isn't very clear about how to solve the problem. Instead, I had to figure out how to code on my own.

The Lectures and given readings are very useful and it is required to read them to complete the assignments which will otherwise be difficult

This will be a 5 star course when all of the technical issues are resolved. More timely feedback from the staff is desirable as well.

A more detailed introduction and guide to python for machine learning would have made this course one of the best out there

The classes are excellent but the exercises are bad. There are some without instructions and we do not know what to do.