The course was interesting. I could learn new things about the application of Machine Learning to the financial industry (specially in weeks 4 and 5). However, I found weeks 1 to 3 extremely focused on theory rather than in practice, giving too much importance to theory over examples based on that could definitely help to better understand the key concepts (e.g. comparing the traditional approach vs the machine learning approach of many financial problems). This said, in general terms, I liked the course.

This course covered a broad range and was therefore a bit shallow. Didactically, it was not as good as the other 3 courses in the programm, and the material in the lectures as not always sufficient for the quizzes.

In my opinion, this was the most advanced course in the series. I liked the examples in the lab, although the explanations were very short - there is a lot of improvement here. But nonetheless, they also helped to digest the material in the lectures a lot.

The course introduced several methods adapted in the asset management world. The idea presented in this course is quite interesting. However, the assessment is somewhat not linked to the lectures and need a lot of guess. The lab session in the course is also a good tutorial to watch and these tutors are well equipped in this area.

The course need to provide a more structured lecture and rework its assessment to link to what have been taught in the lecture.

The content of the course is very interesting and properly explained by the instructors.

Unfortunately, the Lab session with Jupiter are too concise.

Given the complexity of the issues treated, they should last for at least an hour.

Instead, they rarely go beyond 15minutes with the result that the topics of the Labs end up being quickly and superficially explained.

If you compare this course with the previous two courses, you will find disappointment.

Lab session is not well structured step by step, providing you with complex codes without much explanation.

But if you look at the bright side, the course gives you a big picture of machine learning application in the area of investment management.

There are good insights about the applicability of ML techniques in investment management. However, the course structure and material are not at the same level of the other previous 2 courses of this specialization. It is much more harder to follow. Not always theory and lab classes are in sync.

Some live coding and further explanation of some functions should be added, like in the first two MOOCs. This would give the chance of better understanding while practicing on the implementation of ML algos. That way quiz could be enhanced with more implementation of the code questions.

The course is too high level, and provide some introduction to ML. The course materials (i.e. lab sessions, in session quiz questions and rated quizzes) are not accurately drafted. Missing some additional insights on the parameters used for each ML method and its impact

Overall, this course was a lot weaker compared to the previous two of this specialization. While the lecture videos were decent, the lab sessions were just bad. Screenshots of code on slides and unenthusiastic presenters.

The class was okay but not enough detail was provided on the coding process in the labs. They were difficult to follow and had little to do with the material that was tested.

A lot of material but very little explanation on Machine Learning concepts. If the professor and lab instructors took the time to go in depth this could be a good course.

A great basic overview of machine learning methods applied to finance, but the details are sparse. Assessments could be better aligned to objectives.

this module is too theoretical - the other modules in this specialization are more hands on and combine theory and practice better.

did not have an opportunity to play with the code, did not have the chance to build my own models to practise the learned material

I think that the practical lab content was not very well connected to the theoretical part in this course of the specialization.

This is the course that I've liked the least. The labs seem to be almost recommended and not an integral part of the material.

Poor exercises and relatively simple and obvious theory, however, some coding parts and theoretical insights very useful

the course is not well structured, however the content is interesting and the course covers different topics

It would be better if the lectures and the materials correspond with the quizzes and assignments.

It is somewhat disorganized and repeats many topics from previous courses of the specialization.

The Code was not well explained in the lectures however the concepts put forward are valuable

I liked the content, but missed the practical application like in the previous courses.

Lab sessions could explore in more details the coding used for problem solutions.

Informative but a little bit confusing with code parts and some techniques.

Manque d'exercice pratique mais approche très intéressante, trop guidée