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

269 Bewertungen
111 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!

7. Dez. 2020

Excellent course, very helpful for my research work

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51 - 75 von 112 Bewertungen für Python and Machine Learning for Asset Management

von Khursheda F

3. Apr. 2020

did not have an opportunity to play with the code, did not have the chance to build my own models to practise the learned material

von Norbert J

17. Aug. 2020

I think that the practical lab content was not very well connected to the theoretical part in this course of the specialization.

von Francisco V A

17. Nov. 2020

This is the course that I've liked the least. The labs seem to be almost recommended and not an integral part of the material.

von Alex H

19. Aug. 2020

Poor exercises and relatively simple and obvious theory, however, some coding parts and theoretical insights very useful

von Giuseppe

9. Jan. 2021

the course is not well structured, however the content is interesting and the course covers different topics

von David M

31. Dez. 2020

It would be better if the lectures and the materials correspond with the quizzes and assignments.

von Edwin D R D

7. Juni 2020

It is somewhat disorganized and repeats many topics from previous courses of the specialization.

von Bhavya J

26. Juni 2020

The Code was not well explained in the lectures however the concepts put forward are valuable

von Brian H

19. Feb. 2020

I liked the content, but missed the practical application like in the previous courses.

von Pedro B

24. Aug. 2020

Lab sessions could explore in more details the coding used for problem solutions.

von Clément p

6. Nov. 2021

M​anque d'exercice pratique mais approche très intéressante, trop guidée

von Aayush T

11. Juni 2020

The lab sessions could be way better. The quality of tests is bad

von Rui Z

31. Juli 2020

The lab session is not well instructed.

von Chow K M

21. Juli 2021

F​eedback on quiz can be improved.

von Ricardo A T L

25. Aug. 2020

Too General

von Angelo F

14. Mai 2021

Notice the title of this MOOC: "Python and machine learning for asset management". I recognize that the ideas and applications of machine learning proposed are interesting and deserve more study beyond the course, but the content is not adequate for the title.

Some concepts are reasonably explained, but if you did not study anything about machine learning, it will be hard to grasp the opportunities for using ML in finance. So, before taking this course I recommend you do a machine learning course, especially Prof. Ng's course of Machine Learning from Stanford, where all the concepts are clearly developed and explained through 11 or 12 weeks. It is not based on Python, but once you understand the principles it will be easier to implement it at other languages.

Despite it is a Python course, I think it is possible to complete it without even knowing Python. Even though the PhD students made excellent notebooks and presentations, they barely explained Scikit Learn modules. You can complete this course without writing a single script using model selection, preprocessing, pipeline and many other useful modules from Scikit Learn. For instance, you do not need to write a single script to fit a linear regression. How can you expect to apply what you have learned?

Resuming my review, this course does not deliver what it should. The scripts were developed in Python, but if you are not familiar with Scikit Learn, I doubt you can apply the skills you’ve just learned. This subject requires a lot of study and especially practice, but this course does little to reduce this gap. At most, it could be scattered along the other courses of this specialization, like bonuses lectures and labs with ideas about applying machine learning in finance.

von Ruediger K

29. März 2020

Compared to the first two Courses in the certificate, a definite step down. Machine Learning itself is dealt with in the fifth week and of Course, then there apparently isn't enough time to do proper labs.

The lab presentations, each time from a different PhD student with different Levels of enthusiasm for performing this Task, read off the slides. The Princeton Professor is very unspecific in his Statements (just read the transcripts and you will hope that the slides contain real Information).

If the same team would offer the fourth Course in the series, I would drop My plans to complete the certifcate. Instead, I am Looking Forward to the Change in personnel.

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 Jean-Luc 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 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..