The topics covered in this course are really interesting. I learned a great deal by studying various papers covered in this course - Thank you to both instructors!
Excellent course, very helpful for my research work
by Jochen G•
Content is interesting, but course is poorly curated. Material provided (videos, readings and labs) are not fitting well to each other. One gets the feeling that essential parts of the slides were left out, references to past courses don't add up and exam questions are partially unanswered in the videos.
by Ilan J K L•
The course introduces you to some concepts in ML, however there is no audio from the lecturer in the end of the course, making it very tireing to finish. So far this is the weakest course of the specialization and I only finished it to complete the full specialization.
by Marco K•
poor explanations of the python sessions. Unlike first 2 MOOCS where I had the idea that I really learned while doing. Too many errors in coding. Plus set up of all kind of features without too much assistance. This course can be set up much better.
by donald d•
Interesting topics but now well put together. Much more theoretical than previous courses in specialization. Theory is fine but hard to adequately cover topics via 10 min videos. Quizzes were not very useful to learning the material.
by Camilo R R•
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.
by Daniel A C C•
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.
by Toluwalope R•
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
by Luis H C•
Interesting content, but poorly explained. Significant drop in teaching quality compared to the first two courses of the specialization.
by Branson L J X•
Most of the time its just memory work. I didn't feel I learnt practical stuff, sorry.
by Samantha T•
The concepts are not explained clearly by the new team. Labs sessions were poor.
by Nikolay A•
Not completely enough relevant information to pass Quises :(
by Fokrur R H•
Worst course in the specialization
by Henry W•
Professor Lionel is astute and insightful like he was in the first two courses. However the Machine Learning part taught by the other instructor and his PhD students is very lackluster; lacking explanations in both concepts and technicalities. The lab sessions and notebooks are poorly presented, libraries of codes are thrown without good explanation. The quiz questions are not covered by the content of the course, yet they are can be trivially answered, therefore the quiz completely fail to challenge the learners' understanding. As much as I liked the first two courses, I am afraid I cannot recommend this third course.
This course needs a complete rehaul, and NOT be taught by the same machine learning lecturer. Also the labs should preferably be taught in a similar style to Vijay. The combination of Lionel's insight and Vijays thoroughness is just too perfect. Its a shame Vijay cannot teach the 3rd course.
by Lucas F•
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.
by Dinesh M•
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.
by Tathagat K•
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.
by Hernan S L•
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
by Karim M N•
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 ..
by Salvatore T•
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.
by Andreas B•
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.
by Sean S•
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
by Rakesh P•
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.
by Steve B•
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.
by Raf J•
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.