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學生對 北方高等商学院 提供的 Python and Machine Learning for Asset Management 的評價和反饋

212 個評分
94 條評論


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!


Excellent course, very helpful for my research work


26 - Python and Machine Learning for Asset Management 的 50 個評論(共 94 個)

創建者 kitiwat a


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.

創建者 Anas E


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

創建者 Rahul S


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.

創建者 Yaron K


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.

創建者 Fabien N


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.



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.

創建者 HP F


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.

創建者 Long Z


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.

創建者 Moreno C


The content of the course is very interesting and properly explained by the instructors.

Unfortunately, the Lab session with Jupiter are too concise.

Given the complexity of the issues treated, they should last for at least an hour.

Instead, they rarely go beyond 15minutes with the result that the topics of the Labs end up being quickly and superficially explained.

創建者 Kazuto A


If you compare this course with the previous two courses, you will find disappointment.

Lab session is not well structured step by step, providing you with complex codes without much explanation.

But if you look at the bright side, the course gives you a big picture of machine learning application in the area of investment management.

創建者 Rodrigo F R


There are good insights about the applicability of ML techniques in investment management. However, the course structure and material are not at the same level of the other previous 2 courses of this specialization. It is much more harder to follow. Not always theory and lab classes are in sync.

創建者 Eran I


The course is too high level, and provide some introduction to ML. The course materials (i.e. lab sessions, in session quiz questions and rated quizzes) are not accurately drafted. Missing some additional insights on the parameters used for each ML method and its impact

創建者 Alexander D


Overall, this course was a lot weaker compared to the previous two of this specialization. While the lecture videos were decent, the lab sessions were just bad. Screenshots of code on slides and unenthusiastic presenters.

創建者 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.

創建者 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.

創建者 sven h


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

創建者 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

創建者 Norbert J


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

創建者 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.

創建者 Oleksandr H


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

創建者 Giuseppe


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

創建者 David M L H


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

創建者 Edwin D R D


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

創建者 Bhavya J


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

創建者 Brian H


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