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Learner Reviews & Feedback for Python and Machine Learning for Asset Management by EDHEC Business School

3.1
stars
317 ratings

About the Course

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 reviews

ST

Apr 9, 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

May 11, 2022

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

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26 - 50 of 131 Reviews for Python and Machine Learning for Asset Management

By 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.

By Fabien N

Feb 1, 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.

By k. p b

Dec 10, 2020

I think the ideas related to this course are interesting, and in concept it's a great follow-on to the previous two. Unfortunately, I don't believe anyone who doesn't already know concepts and techniques of machine learning will come away from this course with any understanding whatsoever of what they are. I am a mentor for a Coursera specialization in Deep Learning, and I found the description of supervised and unsupervised learning here to be unintelligible. I'll be working through the lab code on my own to learn how to use it for portfolio construction, since I didn't bring that away from completing the course. Really a disappointment after the first two courses in the specialization, which I now question whether I will bother to complete.

By Piet Y

Oct 14, 2021

Lectures were very confusing, poorly explained, poorly structured. Quizzes and tests were extremely bad: questions with often no link to the explained theory, many questions where you could seriously debate about the right answer.

The content was mostly limited to a few cases: a lot about factor shrinking, or better estimations for factor loadings, and a second case of recession prediction. Except for these two, most of the content was vague theoretical examples.

Labs were mixed quality: code was sometimes very clean, sometimes less. The tutors of the labs were sometimes giving a really clear rehearsal or overview of the topics (far better than the gentlemen who give the lectures), but sometimes they just read the text without any contribution.

Overall this MOOC was very disappointing. I have not learned a lot, and most of what I learned I had to google and research myself because the MOOC was too confusing.

By Gustavo G

Feb 12, 2024

This course was horrible, with poor performance on econometric concepts, as well as poor interpretation of ML and inconsistent use of Python.

By Michael L

Aug 11, 2022

This course is excellent! At first one might think that the videos and quizzes in the material gives an first impression and introduction to the topics and that the course ends there, after having completed the quizzess and earned the necessary points for graduating. But, by spending more time actually reading into the suggested research papers, thoroughly going through all the great material (data, notebooks, academic papers) that the team at Princeton and Edhec has been so kindly to provide, the full benefits of taking this course really starts kicking in. I felt a I got a real good introduction to the topics from the videos and quizzes, but as a full-time finance professional, the greatest benefit has been the extensive amount of supporting material that I still use and come back to, to gain new perspectives and inspiration that I can learn from and utilize in my own work!

By carlos j u

Oct 24, 2020

Super interesting, very well explained, with lots of useful resources (links to various papers and textbooks), and, best of all, with very practical, well-annotated notebooks applying the theory covered in the video lessons.

By 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!

By adil r

May 12, 2022

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

By RENATO V M S

Jun 25, 2021

A great course with a Ph Doctoral taste, including amazing and advanced Jupyter Notebooks !!!!

By Rama M

Jan 9, 2021

Thank you, Princeton crew, for this course. I learned a lot, so Thank you. However, learning was not organized. This course is third in the series but has the lowest rating for a reason. I will summarize the pros and cons and provide a roadmap to improve it.

Pros:

a) The academic referenced material is rigorous and requires familiarity with both investments and machine learning topics. This course is certainly not for a beginner. However, having gone through two courses before, one should be reasonably prepared.

b) This course surely provided ML code that can be expanded to conduct further research. As others have said, this course offers building blocks for ML in the asset management area. However, it does not deliver a finished (or semi-finished product).

c) It provides ML code, which most learners cannot develop on their own.

Cons:

a) Too many cooks in the kitchen. Two instructors and five PhD students is a lot to create confusion among labs, videos, and quizzes. There is inconsistency in each week and across weeks.

b) After going through the first two courses (with just two instructors), the bar is set high. Unfortunately, the bar could not be met.

c) Quizzes are horrible. They are vague and unrelated to labs. In the first two courses, lab content was tested heavily. Here concepts are tested. Quizzes need to be rebuilt.

Improvements:

1) Have only one person present (possibly develop) all the labs (like Vijay did in the first two). Then consistency will be maintained.

2) Have quizzes based on labs (not theory). Or make it 80% lab, 20% theory. Currently, quizzes are 80% theory.

3) Rebuild the weekly quizzes from scratch.

By 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.

By Georges A

Mar 9, 2021

The course has seen some improvements since its inception. The subject is still very, very interesting and there is enough materials, code especially for one to explore further on his own. Having a prior knowledge of data science is also probably necessary. Definitely, the tests should be reworked, as they are not adding much value to the understanding of the course.

Overall, it is still a valuable course.

By Roland M

Oct 12, 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.

By Weiwei S

Apr 26, 2022

This is not a watered-down course, and surely is not for beginners as it quickly covers insights instead of details. A very typical course style from top universities. Students need proactively spend time reading and learning materials.

By Hector B

Jan 29, 2021

Very good theoretical discussion and practice The practice part is not given as much importance as it possibly deserves and some of the graded questions are a little ambiguous and not very conductive to learning.

By Alex T

Mar 2, 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.

By kitiwat a

Feb 5, 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.

By Luc T

Feb 18, 2021

Good overview on Machine Learning techniques, need for some basic knowledge in statistics and Python for an optimized experience.

By Anas E

Jan 8, 2021

I would suggest to add the link to the references like pdf docs.

By Ernesto M

Apr 16, 2021

I was thinking very carefully to rate this course and for that I like to refer it first to the article of Claude Shannon (the father of information theory and who worked with Edward Thorp, the first modern mathematician to use quantitative strategies for investments)"A Mathematical Theory of Communication" published in Bell System Technical Journal in 1948 where we are going to take basic elements of communication as we can see in the diagram https://en.wikipedia.org/wiki/File:Shannon_communication_system.svg.

An information source that produces a message

A transmitter that operates on the message to create a signal which can be sent through a channel

A channel, which is the medium over which the signal, carrying the information that composes the message, is sent.

A receiver, which transforms the signal back into the message intended for delivery

A destination, which can be a person or a machine, for whom or which the message is intended

A noise source that can perturbate and corrupt the message.

And second, to the DIKW hierarchy, wisdom hierarchy, knowledge hierarchy, information hierarchy, and the data pyramid as https://en.wikipedia.org/wiki/File:DIKW_Pyramid.svg.

As a conclusion although the information source was a high level, interesting and important, because of the fact that the communication channel was not efficient enough to transmit it, the destination did not receive that information correctly. Related to the flow diagram https://en.wikipedia.org/wiki/File:DIKW_(1).png we can barely knew "how" but we did not went deeply into"why" and further.

On the other hand , the other 3 courses of this specialization arrived to answer "why" and "what is best" questions.

I hope a full revision of this course be performed, in particular: its methodology, the way lessons were taught, the replacement of the non professional LAB's lecturers, the duration of the lessons, etc.

By 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.

By JONATHAN A

Apr 12, 2020

The course was interesting. I could learn new things about the application of Machine Learning to the financial industry (specially in weeks 4 and 5). However, I found weeks 1 to 3 extremely focused on theory rather than in practice, giving too much importance to theory over examples based on that could definitely help to better understand the key concepts (e.g. comparing the traditional approach vs the machine learning approach of many financial problems). This said, in general terms, I liked the course.

By HP F

Jun 9, 2020

This course covered a broad range and was therefore a bit shallow. Didactically, it was not as good as the other 3 courses in the programm, and the material in the lectures as not always sufficient for the quizzes.

In my opinion, this was the most advanced course in the series. I liked the examples in the lab, although the explanations were very short - there is a lot of improvement here. But nonetheless, they also helped to digest the material in the lectures a lot.

By Long Z

Apr 5, 2020

The course introduced several methods adapted in the asset management world. The idea presented in this course is quite interesting. However, the assessment is somewhat not linked to the lectures and need a lot of guess. The lab session in the course is also a good tutorial to watch and these tutors are well equipped in this area.

The course need to provide a more structured lecture and rework its assessment to link to what have been taught in the lecture.