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Bewertung und Feedback des Lernenden für Python and Machine Learning for Asset Management von EDHEC Business School

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282 Bewertungen
118 Bewertungen

Über den Kurs

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....

Top-Bewertungen

ST

9. Apr. 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!

AR

11. Mai 2022

Very nice course sharing many types of knowledges around data / cleaning / type of data / several algorithms / organised Python coding

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76 - 100 von 119 Bewertungen für Python and Machine Learning for Asset Management

von Marco D

13. Apr. 2020

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

von NORIAKI S

26. Sep. 2020

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.

von Maximiliano M

6. März 2022

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.

von Loc N

2. Jan. 2020

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.

von Hilmi E

30. Dez. 2020

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..

von Jochen G

29. Mai 2020

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.

von Tim R

11. Feb. 2022

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.

von Ilan J K L

18. Mai 2020

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.

von Marco K

22. Juni 2020

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.

von donald d

25. Nov. 2020

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.

von Camilo R R

8. Jan. 2022

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.

von Daniel A C C

23. Aug. 2020

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.

von Toluwalope R

17. Aug. 2020

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

von Luis H C

15. Nov. 2020

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

von Branson L J X

10. Juli 2020

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

von Samantha T

9. Mai 2020

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

von Nikolay A

13. Mai 2020

Not completely enough relevant information to pass Quises :(

von Fokrur R H

10. Aug. 2020

Worst course in the specialization

von Henry W

28. Apr. 2020

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.

von Lucas F

26. Apr. 2020

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.

von Dinesh M

27. März 2021

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.

von Tathagat K

29. Mai 2020

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.

von Hernan S L

22. Apr. 2020

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

von Karim M N

8. Aug. 2020

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 ..

von Salvatore T

24. Feb. 2021

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.