I leave this review after I have completed the specialization of all 4 courses.

The first two courses of the specialization are somewhat useful in understanding some concepts, even though they are apparently not good enough and often full of bizarre design.

Now here comes the third one, with much worse experience. The teaching quality is as mediocre as the first two. The instructor has been overly obsessed in his physics analogy without being aware what he actually needs to teach. The content is unnecessarily theoretical and makes one lost. I appreciate that this is intellectually interesting, maybe to some people. Nevertheless, it does not meet my expectation at all. The assignment of this course is open-ended and continue to be irrelevant from what the videos are.

Conclusion: The course is not mature enough. If someone wants to learn machine learning in finance with efficiency and practicality, he or she should consider other options instead of this specialization/course.

The content of the courses in this specialization is astonishing, very nice.

However this course should be removed from Coursera. Staff are ghosts, no responses whatsoever.

Lectures are purely mathematical, there are no visualizations on how they fit to the code in the assignments. No explanations on how the code should work, no explanations on how we should provide our answers, graders are buggy. This course could be one of the best in Coursera, but it fails miserably. waste of time and money.

The lectures are interesting and the exercises are challenging, yet engaging. However, the technical support for assignment submissions is abysmal. Course staff are unhelpful, if they respond, which they often don't. I was unable to complete the course due to technical problems, don't pay for this scam.

Do not undertake this course unless you are i. proficient in python and the subject matter already and ii. have 3 friends to take the course at the same time because otherwise your final assignment will not get graded.

I think the course is actually very good. I think it's received mixed reviews because I can imagine it would be a lot to take in if you try and complete the course in the allocated time. However, if you do dedicate more time to it and study the support material (the pdf's at the bottom of each weeks content) then things do start to become more clear. To get something out of this course I would recommended adjusting your expectations (in terms of time scales) and taking you're time (I've been doing it on and off for a couple of months) to complete the course.

Tests at the end of the videos cut what Igor is saying and they are often about the following video.

The assessments have no interaction with what we are supposed to learn, the 10-people staff is never online and never answers any message, and you need far more finance knowledge than advertised.

staff is missing. exercises do not correspond with what it is seen on the videos.

Do not take this course if you know nothing about finance.

Final assignment was too vague and doesn't seem that useful.

assignments are very frustrating, unclear instructions, no help from staff/moderator/instructor, technical issues that are not resolved, etc. it just feels like the course was rushed to production and they let the students debug it. the final assignment cannot be submitted, everyone is stranded here and unable to complete the course.

the simulation method may not work well in reality. If use other methods like deep-q-learning, it will be much better

This course is based on 3 papers of the presenter. Good overview of reinforcement learning in finance, but the presentations are not well formatted, and coding exercises are not helpful at all to improve. Course project is irrelevant to reinforcement learning. It's a mediocre course.

Excellent overview of reinforcement learning with applications to option pricing and stock trading..To benefit fully from this course, a good command of python and various libraries for machine learning/data science is essential...

Course content is okay but the coding exercices are not on point at all.

There is too much focus on quantitative financial analysis, and not enough time on explaining RL. Simple financial examples are beneficial to understand the practical use of RL in finance, but I felt that this course had a strong bias towards finance. I feel I still need to do further reading in order to apply RL in my work. The assignment did not supply all the data required to perform the tasks. The peer grading process did not work, we had to rely on sharing links in the discussion section to get our assignments graded.

The "Reinforcement Learning in Finance" course delves into applying fundamental reinforcement learning (RL) concepts in finance, including option valuation and asset management. It guides students through applying RL to financial issues, using algorithms like Q-learning and Inverse RL for a capstone project. It provides practical financial applications of RL, albeit with a significant mathematical demand, which makes it challenging for those who need a strong base in the necessary prerequisites. Anyone interested can find a complete review of the course at: https://www.linkedin.com/posts/dsolis_machinelearning-quantitativefinance-mathematicalfinance-activity-7172615209851822080-UE26

Excellent course. The peer reviewed evaluation is very interisting and it is definitely worth the time to do it in detail but does not take two hours with luck a week.

This course is excellent!

Great delivery

The material is very interesting and relevant for me. The exercises are good, but the automated testing was too 'non-flexible' leading me to have to search in forums and realize it was dependent on a specific order of extracting random numbers. I would have liked more practical examples in the talk rather than endless list of equations which are simple by themselves