It doesn't teach you how to build the algorithm or the details of it and it ignores the good practice of the two previous courses of teaching you step by step. not recommended course.

Compared with the first to MOOCs this one is not so easy to understand since is most theory and the python lessons are given in 15 minutes with a huge of material to read.

It wasn't as good as the other courses. We didn't really get many useful lab sessions and opportunities to really understand the machine learning side in practice

Interesting content, but poorly explained. Significant drop in teaching quality compared to the first two courses of the specialization.

Disorganized notebooks, glitchy presentations, rushed through complex lecture materials.

Most of the time its just memory work. I didn't feel I learnt practical stuff, sorry.

The concepts are not explained clearly by the new team. Labs sessions were poor.

Not completely enough relevant information to pass Quises :(

Worst course in the specialization

The previous 2 modules were really good and I learnt a lot from both a theoretical and a practical point of view. Unfortunately, this was not the case on this one. There is significant room for improvement on both the structure and content of this module. A few issues:

The content is a bit confusing with a mix of what was taught on the previous two courses and new content. The quizzes are quite generic and don't cover the code given.

The intuition behind the statistical methods taught is just not there. You get the formulas but you wont really understand what is driving the methods. You don't get the economic intuition of the ML models applied to financial applications. I don't feel capable at all to use what was taught in outside applications.

Lab sessions lack quality and are not consistent with the previous two courses, unfortunately. A lot of space to improve here.

Compared to the other courses in this specialization, this course has very poorly organized materials especially when it comes to lab sessions and the pertinent resources. Quite unprofessionally, ineffectively organized resources, if I may say so to drive home the point. Because for most of the audience you are targetting via an online course: the following are most important: time efficiency. organization of materials, actual/real application vs just some theoretical familiarity. This course scores extremely low.

The quizzes are laughable at first, and annoying eventually. Extremely ambiguous questions and options; and very often during the quizzes as well as during labs/lectures unnecessary jargon is brought in.

Also annoying are the sections that are just repeats from the earlier modules.

This is one of the worst MOOCS I've ever seen. I did ML by Andrew Ng without much background in the subject and was still able to follow and assimilate everything.

This MOOC is all about the prof and the students just showing you a haphazard, mixed up preview of what they know. They don't know anything about teaching, anything about explaining, anything about documentation and anything about framing questions for the quiz. The quiz sounds like something under-graduate teaching assistants have prepared by just looking at the videos without even understanding them.

And this MOOC is a massive contrast from the ones conducted by Vijay where he explains line by line, how to code the ideas that he teaches.

I'm thoroughly disappointed by EDHEC and Princeton.

A very bad course.

I have incorporated 0 concepts from the ML side regarding python application. The lab sessions are really because no formula is explained as Vijay did previously in MOOC 1 and 2. I am really disappointed with MOOC 3 because I had higher expecations...but when I started I realized that I was not a good course. All my critics are regarding the ML part of the course and his teacher and the lab sessions. There is no background explained and the professor just pastes huge formulas in the background with huge texts and it is impossible to follow. Also the grading system is a mess.

I will not recommend this course

Horrible !

Such a waste of time... the labs are neither explained or commented... one very important section doesn't even have a lab !

The instructor, John Mulvey, cannot explain in the lectures -- he isn't even consistent with his notation in the slides

The people who built this MOOC were very lazy, and not thorough...

Don't take this course, you will waste a lot of time scratching your head, trying to figure out what the instructors are saying -- I am not the only one who thinks so, everyone is complaining in the course discussion forums ..

I regret to say that this course is not at all on the level of the previous two courses of this specialization. Despite the material is very interesting, it is presented in a poor way. I would rather make less and better, in order to use the full potential of the Instructors. A positive note on these courses should be given to the assistants, that have been always very helpful, and they provided a fantastic guidance to everyone so far. I am looking forward to do the next course of this specialization.

Awful class. I have taken a few ML/DL lessons from Coursera and this is by far one of the worst presentation on ML. The videos are painful to watch, the content is incoherent, the quizzes are poorly constructed. Everything about this class is just substandard. If you are interested in ML in finance, just audit this class, and download the Jupyter notebook to see example code and key concepts. The videos are pure waste of time.

As much as I was impressed by the first two courses, this one has been a huge disappointment. The material is not taught in an accessible manner, there are literally not explanations of the python code and despite having completed the first two instalments, this one felt like a huge step-change in what is required. These are complex topics and it is key to explain them in a simple, comprehensible way, not in a complicated manner.

This course was the worst course that I have taken on the coursera platform. The videos were rambling, that only gave incomplete overviews of the theory of machine learning. There was very little instruction on actually building machine learnings model in python. The code examples provided were buggy and insufficiently explained for such a complex subject

I was really hopeful and looking forward to another great course after experiencing the first two MOOCs in this specialization. I have to say that between the extremely ambiguous questions in the videos as well as in the exams and the extreme lack of detail in explaining any of the code, I have never seen a more disappointing course.

Unfortunately this course felt like a beta / first version especially when compared to the first 2 courses in the specialization. The quizzes are particularly frustrating and the Labs could be better integrated with the theoretical material.

I would still strongly recommend the specialization.

The first course of this specialisation was excellent. Lab sessions and python notebooks very well explained. Second course was a little worse but still good enough. This course is not good. Most of the Video lab sessions are terrible.

Quality of lab sessions was consistently and significantly poorer than the first 2 courses. Outputs were not consistent with what was discussed in the lectures. Got different results from that shown in lectures.

very hard to follow the lab. The material is soooo jumpy logically. It start with intro python in the first slide and jump to complicated functions in the later slides. It never walk novices through the steps.

Disappointed with the quality of this course. No depth in using Machine Learning for Asset Management. Lesson and lab sessions are poorly put together. No quality control. Waste of time and money.

Very bad course for machine learning application part. Feel like I've learned nothing. The lab session has no explanation for the details of ML techinques.