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

303 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....



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!


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 126 Bewertungen für Python and Machine Learning for Asset Management

von Ashish K

10. Feb. 2022

This course left with a lot to be desired. First the repitions from MooC 1 & 2 were substantial. Course rushed through the Machine learning principles (i was ok as i did a course by Prof Ng). The Phd students seemed like making a class presentations and were mostly just reading out the text, a lot of time repeating the theory. We learned almost nothing from the lab sessions, which were very important for practical knowledge. Hope the lab sessions are repeated by Mr Vaidyanathan. Overall, this was the course i subscribed this speacialisation for, and am left disappointed. I would still recommend others to take the course.

von Rehan I

9. Apr. 2020

Quite a disappointing course after the first two MOOCs, which were excellent.

Machine learning material was not explained well in the videos. I suggest Andrew Ng's Machine Learning course on Coursera instead for a much better grounding in ML.

Labs were very poor: some of the notebooks provided don't even execute, the videos were just high level overviews of the labs instead of taking the student through them like in MOOCs 1 and 2, and no programming skill was tested in the quiz. The labs part of this course fails on its promise to equip the student with the skillset to build similar models of their own.

Bring Vijay back!

von Tobias T

13. Sep. 2020

Very disappointing course compared to the first two courses of the specialization. It is nice for an overview of the techniques, but the techniques are not really explained. Neither the often mathematical screenshot of a paper, which you see for 10 seconds, nor the lab sessions help in understanding what is going on. Python code is not explained like it was from Vijay, you only see the output from a scipy- or Princton-written function (with the hint: "look into the documentary"), the instructors read what is written on the slides and that's it. No chance to reproduce anything or actually learn the stuff.

von Christopher B

28. Mai 2020

A lot of disjoint information about algorithms and finance was presented in a flashy way. Only about 10--20% of the course was genuinely about implementation of machine learning. All the code that was written was just thrown in front of you via pre-made note books without much explanation as to what was going on in terms of machine learning. Out of the four courses in this specialization, it was definitely the worst. Also, the assessments didn't really reflect the material that was covered at all. They were a struggle to pass without going back trying to dissect all the material.

von JL B

7. März 2020

A disappointment, especially after the first courses which were great. I missed the labs by Vijay. The Princeton parts were interesting if I want to be kind but not really useful. Too much material on the slides, hard to follow while the lecturer was speaking. And in a course about Machine Learning I expect more code, examples and results during the lectures. The quizzes were ambiguous, often non numerical and didn't rely enough on interaction with the notebooks.And what about the sound ? very often only in the right speaker. Too bad, the subject is so exciting...

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


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 Alex H

27. Juli 2022

There is a lot of interesting information here. However the jump from the lectures to the labs is gigantic. (And it's not the coding). The high-level explanations about ML concepts in the lectures were great, but the indepth breakdown of the models in the labs covered, what seems to be, hours more of material. I could not follow it.

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 Anon

20. Jan. 2023

Disorganized notebooks, glitchy presentations, rushed through complex lecture materials.

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