Chevron Left
Вернуться к Python and Machine Learning for Asset Management

Отзывы учащихся о курсе Python and Machine Learning for Asset Management от партнера Школа бизнеса EDHEC

3.1
звезд
Оценки: 286

О курсе

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....

Лучшие рецензии

ST

9 апр. 2020 г.

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

11 мая 2022 г.

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

Фильтр по:

26–50 из 123 отзывов о курсе Python and Machine Learning for Asset Management

автор: carlos j u

24 окт. 2020 г.

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.

автор: Shahpour T

10 апр. 2020 г.

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!

автор: adil r

12 мая 2022 г.

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

автор: RENATO V M S

25 июня 2021 г.

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

автор: Peter H S

2 авг. 2022 г.

E​xcellent content! Great programming notebooks from Princeton University.

автор: Rama M

9 янв. 2021 г.

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.

автор: Золкин Т А

13 июля 2020 г.

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.

автор: Georges A

9 мар. 2021 г.

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.

автор: Roland M

12 окт. 2020 г.

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.

автор: Weiwei S

26 апр. 2022 г.

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.

автор: Hector B

29 янв. 2021 г.

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.

автор: Alex T

2 мар. 2020 г.

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.

автор: kitiwat a

5 февр. 2020 г.

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.

автор: Luc T

18 февр. 2021 г.

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

автор: Anas E

8 янв. 2021 г.

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

автор: Ernesto M

16 апр. 2021 г.

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.

автор: Rahul S

30 июня 2020 г.

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.

автор: Yaron K

27 сент. 2020 г.

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.

автор: JONATHAN A G

12 апр. 2020 г.

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.

автор: Hp F

9 июня 2020 г.

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.

автор: Long Z

5 апр. 2020 г.

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.

автор: Moreno C

6 июля 2020 г.

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.

автор: Kazuto A

2 авг. 2020 г.

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.

автор: Rodrigo R

15 нояб. 2020 г.

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.

автор: Kostas T

24 апр. 2021 г.

Some live coding and further explanation of some functions should be added, like in the first two MOOCs. This would give the chance of better understanding while practicing on the implementation of ML algos. That way quiz could be enhanced with more implementation of the code questions.