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!
Very nice course sharing many types of knowledges around data / cleaning / type of data / several algorithms / organised Python coding
by Marco D•
it ain't at the same level of the previous MOOC. There is no lab session for PCA/Clustering/Graphical Analysis that happens to be one of the most important topics for this MOOC; as a result, it should have been properly covered. Previous MOOCs are perfect, this one is not. Eventually, I would have expected this MOOC had spent more time going in details through coding part: lab sessions are not as effective as those of the previous MOOCs. I learned lots of useful techniques though, so it is worth in the end
by NORIAKI S•
Slides and lectures (John's part) consists of ambiguous and high level remarks without concrete examples to help learners understand.
It would be better if we have the slides as files so that we don't have to scribble them. We cannot retain high level explanations in our mind by just listening and looking at the slides!
Quizzes were terrible. I wonder if the quizzes were prepared after checking the content of the lectures at all.
by Maximiliano M•
The quality of the lab sessiones is really bad compared to previous modules. They are not explained properly and some important features were left aside or poorly taught such as coding structure. They tend to say "This is the way...". We are not MANDALORIANS... Another problem is related to the reading material, ie. week 5 reading list. It is not provided by the course and it's not available for free.
by Loc N•
The course feels chaotic and unplanned, unlike the previous two courses in the series. This course glosses over on some of the important technical details, while repeats too much basic or non-technical information. It also seems the course outsources the teaching to PhD students and readings, which causes further inconsistency.
by Hilmi E•
This course lacks the quality of the first two courses of the series: presentations are poor, repetitive, sometimes trivial with unreadable visuals..Quizzes are childish at this level..
The labs contain good material but are poorly packaged(not fully debugged, multiple versions,unreadable video presentations) and presented..
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 Tim R•
Repeats some of the concept of the first two courses of the specialization. Further, the Lab-session are a bit miserable. Compared to the first two courses the test are fairly straight forward and easy. In general, I did not nearly enjoy this course as much as the first two.
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.