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Python and Machine Learning for Asset Management に戻る

EDHEC ビジネススクール(EDHEC Business School) による Python and Machine Learning for Asset Management の受講者のレビューおよびフィードバック



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....




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!



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


Python and Machine Learning for Asset Management : 51 - 75 / 119 レビュー

by Adam C


The class was okay but not enough detail was provided on the coding process in the labs. They were difficult to follow and had little to do with the material that was tested.

by Karl J


A great basic overview of machine learning methods applied to finance, but the details are sparse. Assessments could be better aligned to objectives.

by sven h


this module is too theoretical - the other modules in this specialization are more hands on and combine theory and practice better.

by Khursheda F


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

by Norbert J


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

by Francisco V A


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.

by Alex H


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

by Giuseppe


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

by David M


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

by Edwin D R D


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

by Bhavya J


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

by Brian H


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

by Pedro B


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

by Clément p


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

by Aayush T


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

by Rui Z


The lab session is not well instructed.

by Chow K M


F​eedback on quiz can be improved.

by Ricardo A T L


Too General

by Angelo F


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.

by Ruediger K


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.

by Ashish K


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.

by Rehan I


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!

by Tobias T


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.

by Christopher B


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.

by Jean-Luc B


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...