创建者 Semant J P•
Jan 13, 2020
First about me - I been deeply involved in data science, and machine learning and trade on the financial markets. So, in addition to solid academic credentials, I have a real life practical experience. I took this course to check if there were some additional skills I could learn.
I was sorely disappointed. This is a completely useless course.
The first two courses in this specialization were amazing. This has been the worst organized and least practical course. As other reviews have pointed out, academic research on regime filtering was pandered out as machine learning in finance. I was expecting to learn practical instances of using supervised, unsupervised, deep learning used in finance. There was nothing of this sort.
I have never seen Q-Q plots being used in investment/hedge funds - we talk about annualized returns, standard deviation, Sharpe ratio, and drawdowns. These statistical markers were used by Vijay in the first two courses. Not here.
This course needs to be rebuild from scratch - and Vijay needs to be brought back in for real practical application of ML in financial services.
创建者 Keith W•
Nov 20, 2019
The jump in Python programming was not handled well - it was far too complex and an order of magnitude more complex than anything that had come before. I enjoyed the theory, but feel lost with the Python component. A 12 minute lab session with a Princeton grad student was not nearly enough to grasp the material. Bring back Vijay who is excellent in teaching Python!
创建者 Soheil S•
Jan 20, 2020
It was a terrible experience taking this course. Despite the two first courses, this one is disappointing! the ML instructor does not offer any useful material and all of the ML lectures contain ambiguous and useless material. The worst part is the quizzes. the multiple choices include ambiguous answers and that you should choose more than one and the ridiculous part is that either you would get the full mark or nothing! even if you choose some choices correctly and you never know what was your mistakenly chosen choice! I've tried the week 2 quiz for 9 times and have not been yet successful to pass it.
It's overwhelmingly complicated and unclear.
I didn't expect such a terrible course from EDHEC Business School and Coursera!
创建者 Mirkamil G•
Apr 18, 2020
Interesting thema but bad cunstruction!
As I was enrolling in this course, I was excited to thinking about I can solve financial problems with ML on my own. But I must say I am totally disapointed after I finished it.
This is really berrible copparing to the first two courses from this specialization. The Master Vijad was so inspireble, he should come back and explian us how the Leb-Sessions was build and how can we use the programms, specially the Leb-Session for "Clustering and Grafical analysis for diversification" should be add on.
The PHD students were just reading what was happening on the slids from Prof. and even so, they read it wrong several times.
Acctually, this course can split to more than five weeks and evey details should explained specifically like the first two courses in this specialization. Maybe the Prof. Mulvey shold also find out this construction was kind of tight for someone who come from whith data-analyst or data-scientist background.
If the Master Vijad come again, I would think about to take this course one more time!
创建者 Ziheng C•
Dec 24, 2019
Personally, this is the BEST online course I have ever seen. For students with basic knowledge in machine learning and finance, this can help them improve a lot, especially helping them to combine these two things. In addition, the viewpoint of Professor John Mulvey is sharp and indicate directions for applying ML in investment management courses. Best course ever.
创建者 Andrea C•
Jan 09, 2020
John part is really confusing and not well explained. his slides and very high level and labs are very low level with basically no explanation. The rest of the course is fine.
创建者 Serg D•
Nov 23, 2019
Well, that was disappointing. What was the point bringing Princeton into this? Looks like edhec does not have in house ml experience. I did not find this course, exercises and labs to be practical at all. As another commentator said bring back Vijay!
创建者 Francisco C•
Jan 17, 2020
I learned about how can be used the machine learning in asset management, but to much theory and nothing practical. We received the lab done, and could not understand how implement. I missed the lab of the first two courses.
创建者 Dirk W•
Feb 05, 2020
Honestly, for this course, in the present state of work in progress, I can't give more than 1 star. Not well-constructed course, no right balance between theory and lab sessions. Theory on Machine Learning is on basic high-level concepts. Even the visual format of the lecture videos is irritating. Lab sessions are not always present, or not explained in a detailed manner, which is really a problem.
Stars are also missing because of a few frustrating quizzes and because of the lack of (quick/relevant) responses or answers of the moderators in the forum.
Please rework this course, with the high-quality other courses of this specialisation as example; please also take also the remarks of the students in the forum into consideration.
创建者 Nicholas P D•
Mar 15, 2020
The first two courses were very well done. This one is not even close to helpful. In the first two courses the Jupyter lab sessions were my favourite and really brought all the concepts together. The prof would go step by step through the code, even if it took an hour. In this course, I completely dread the lab sessions. They are only 15 minutes long and dump 200+ lines of uncommented code on you to deal with yourself. Also, it would be really nice if they could add presentation slides. All the lectures take twice as long because I have to pause and write down the formulas. It's sad because I used to look forward to learning, now I am just here to finish the specialization.
创建者 Michinori K•
Feb 13, 2020
This course is clearly of lower quality than the previous two courses of the Investment Management with Python and Machine Learning Specialization. Quiz is too ambiguous and very painful to pass.
创建者 Tommy L•
May 21, 2020
This course is absolutely horrible. Large majority if not all of the content is just fluff. The quizzes have very little to do with the lectures or labs. Also some of the quiz questions are just wrong or are irrelevant. The code in the labs is low quality. The lecturer is bad at teaching and explaining concepts.
Mar 12, 2020
This is the worst course of this 4-courses specialization due to the useless lab-session. I miss VJ so badly....lol
创建者 Erick I A•
Apr 10, 2020
It was an amazing course, but definitely I will suggest for you that want to take this course to have a knowledge of investment, statistics and python. I totally recommend this course.
创建者 Shahpour T•
Apr 10, 2020
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!
创建者 Золкин Т А•
Jul 13, 2020
A good course overall but there are significant drawbacks: test questions are sometimes intimidating and overly on theory while Python code is barely covered in the Lab sessions. The papers and materials provided can be of great use for people ready to dive a bit further. Still I think this course lacks a pair of short videos that will cover Python code in detail for learners without strong background in ML and coding. Nethertheless, I don't want to give a poor mark to the course.
创建者 Alex T•
Mar 02, 2020
would be good to focus more on the jupyter notebooks and less on multiple choice. Really interesting notebooks and quite advanced / technical material which deserves more time and coverage.
创建者 kitiwat a•
Feb 06, 2020
Good concepts to touch but lack on coding in granulality example. But overall, I'm get a good example how to implement machine learning technique to finance perspective.
创建者 Anurag J•
May 31, 2020
Please consider adding additional videos for the lab sessions, as one can not gain the Machine Learning python coding skills from PPT slides!
创建者 Jerry H•
May 18, 2020
What I found to be really valuable and potentially useful were the examples/case histories of how the various machine learning techniques to portfolio management. For me, the most valuable learnings were, regularized regression to compute factor loadings, application of PCA/Clustering and Graphical Approaches to maximize portfolio diversity, and scenario/regime based portfolio models. I fully intend to do some follow-up work in applying those techniques to my personal investment management. So while perhaps not as learner-friendly as the previous two courses, I think the subject matter will prove to be far more valuable if one invests the time after the course.
I think if you want a better understanding of the many machine learning techniques, you might be better served to take a course specifically focused on that. I found the treatment of these techniques, insufficient to gain a solid conceptual understanding of the techniques. With that in mind, the course might be improved be spending even less time on introducing some of the basic machine learning methods / and traditional models, that are well covered elsewhere, and more time on the case histories, and application of the methods to portfolio management and investing.
创建者 Rahul S•
Jun 30, 2020
I must say its been a long journey since first MOOC in this specialization. I had great learning and someone having no past programming background has acquired a lot in this specialization. Fortunately, the first two MOOCs were really well connected since Dr. Vijay Vaidyanathan has explained things so well that at least I could understand the concept as well as the implementation in the real data.. I was really excited for this MOOC but instead of focusing more on the practical part things were taken fast and solely in theory. I wouldn't say it was bad but the lab session could have been more engaging and explanatory like the first two MOOCs since it would have been helpful for non-programming background finance professionals.
创建者 Fabien N•
Feb 01, 2020
I have been more and more frustrated with the course that became less and less explanatory, but more and more descriptive. I still find the topics very interesting, and the first two MOOCs were really amazing, but I find this one much less clear and giving us much less understanding of the coding part. What would be really great would be to get a full description of what the code does, at least much more detailed than at present. As an example, no code was even provided for PCA and graphical networks, that's quite disappointing.
创建者 JONATHAN A G•
Apr 12, 2020
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.
创建者 HP F•
Jun 09, 2020
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.
创建者 Long Z•
Apr 06, 2020
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.