Good because it gives a high level good overview of ML in Finance, SVM and Tensorflow.

However, Some examples are very easy and some have been made difficult by providing no references. Tobit regression was very vague. No links to proper reference. Neural Network was the example from Geron's Handbook but there were errors in the custom function that was defined.

More mathematical depth is required.

extremely hard to follow, but better than when it originally came out. I had signed up after numerous ML courses and tried to skip to the later courses in this specialization. I got stuck trying to implement some crazy equations. I'm ok with looking up api methods, but the need to look out for reshaping is troublesome because it's inconsistent throughout the course. Overall, hard to follow.

Good overview of ML and some basic applications to finance.

The pace is very good for people with some training in statistics and maths.

The assignments, however, are not particularly clear and with some obvious errors. There's room for improvement in the description of the exercises as well as including some tests to verify that you're getting the correct output.

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

Course material is good and a rating of 4 stars or more would have been a fair one, if it was not for very poorly designed and ill prepared assignments. The teaching staff really need to step up a level or two for the assignments.

The course content is good and that the only reason, I am still sticking with this specialization.

Having done Andrews Ng's course, the pedagogy of this course is not at the same level. The practical work is obscure and poorly guided. I had a hard time doing them knowing that I work in artificial intelligence and that I use these libraries on a daily basis. I am very hesitant to do the next 3 courses.

The assignments can improve a lot. The jupyter notebooks have no clarity in instructions and most of the time we have to struggle to find exactly what is expected from our code.

The specialization has a lot of potential, anchored only by the lack of the quality of the assignments.

It's a good course but the homework is poorly designed with unclear instructions. Moreover, it's better to get familiar with Python before start this course. The suggested book "Hands-On Machine Learning with Scikit-Learn & TensorFlow" is a very good resource.

Great contents. Excellent topic.

But poor explanation especially in coding assignment.

The assignment includes every coding stuff you need to learn in this course. But there is no explanation about it. You can learn theory from prof. But...coding...?

Very straightforward lectures followed by complex notebooks at a significantly higher level. Given that the labs are using a deprecated version of TensorFlow, with regret, I won't be pursuing this specialization any further.

It is a very broad overview of the machine learning topics but very little about the applications in finance. It wont give you a foundation in machine learning nor any useful insights about financial applications...

Cons: overall content is good. Pros: when you release something (software or scientific article) you often do rigorous testing. Why not to do it with your Jupyter Notebooks? I do not understand it.

Some of the labs/programming assignments were difficult to complete. There is not much guidance in some sections, and some python packages that were never demonstrated in the videos were used.

Lab exercises need improvements, as most of the time you need to guess what the real question is and they are not even covered in lectures!

Otherwise the course has lots of potential.

Good Thing -> Problably the best explanations for gradient descent that I have ever seen.

Bad -> The exercises are very difficult for the level of explanations provided.

The lectures were very good, but the assignments lacked supporting material. Also, most of the further reading was behind a paywall or the links had been removed.

Content of the class is really good but technology/support is deplorable (Had to wait 3 weeks before the assignments got fixed by the support staff)

Not very related to finance plus most of the tasks are easy to complete, but hard to understand what needs to be done.

The lecture is ok but lacks of details. The project is not well designed and hard to complete without much guidance.

There is always something to be fixed in the assignments... Great content and relevance though.

Great content, but the labs are difficult to understand and often unrelated with the content.

There should be more explanations of codes in the video courses. Thanks.

Proper structure is absent but a lot of potential inside the course.

great content but horrible exercises with misleading instructions

some python notebook has bugs, wasting time for me to fix