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Avis et commentaires pour d'étudiants pour Python and Machine Learning for Asset Management par EDHEC Business School

169 évaluations
74 avis

À propos du cours

This course will enable you mastering machine-learning approaches in the area of investment management. It has been designed by two thought leaders in their field, Lionel Martellini from EDHEC-Risk Institute and John Mulvey from Princeton University. Starting from the basics, they will help you build practical skills to understand data science so you can make the best portfolio decisions. The course will start with an introduction to the fundamentals of machine learning, followed by an in-depth discussion of the application of these techniques to portfolio management decisions, including the design of more robust factor models, the construction of portfolios with improved diversification benefits, and the implementation of more efficient risk management models. We have designed a 3-step learning process: first, we will introduce a meaningful investment problem and see how this problem can be addressed using statistical techniques. Then, we will see how this new insight from Machine learning can complete and improve the relevance of the analysis. You will have the opportunity to capitalize on videos and recommended readings to level up your financial expertise, and to use the quizzes and Jupiter notebooks to ensure grasp of concept. At the end of this course, you will master the various machine learning techniques in investment management....

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1 - 25 sur 74 Avis pour Python and Machine Learning for Asset Management

par 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.

par 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!

par 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!

par 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!

par 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.

par 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.

par 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.

par 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!

par 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.

par 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.

par 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!

par 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.

par 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.

par 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.

par Xinhao

Mar 12, 2020

This is the worst course of this 4-courses specialization due to the useless lab-session. I miss VJ so

par Antony J

Oct 14, 2020

I thought this was an excellent course that covers a wide range of applications of machine learning methods to investment management that have been published in top peer-reviewed finance and statistics / operational research journals. There is enough material in the excellent Python labs to lay the foundation for at least 6 months' worth of further research study.

A cautionary note: this is fast-paced and will most benefit learners who already have a foundation in machine learning from, for example, Andrew Ng's famous course. I also recommend first completing the preceding two courses in the specialization. This could be a tough course to take in isolation.

par 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.

par 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!

par 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.

par Roland M

Oct 13, 2020

The overall topic of this course is great and very current.

I think the lab sessions can be improved. The Python supporting material is not always available and/or topics are covered at a very high level in the lab sessions.

Given the complexity of some of the sections, it may be worth considering extending this course (from 5 weeks to 7-8 weeks?) so that topics can be covered more in depth.

par 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.

par 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.

par 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.

par Yaron K

Sep 27, 2020

The subjects addressed in the course, such as models to identify crash regimes, are interesting and important. It points out important implementation issues in Machine Learning like regularization, k-fold validation to choose hyperparameters, and introduces multiple ML algorithms and methods (OLS regression, Logistic regression, Decision trees, Boosting, Graphical analysis functions).

Unhappily the explanations are convoluted and the Python Notebooks only cursorily explained.

Gave the course 3 stars because the Notebooks are 5-star.

par 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.