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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

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


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!


11 мая 2022 г.

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

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51–75 из 123 отзывов о курсе Python and Machine Learning for Asset Management

автор: Eran I

5 авг. 2020 г.

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

автор: Alexander D

1 мар. 2020 г.

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.

автор: Adam C

23 дек. 2020 г.

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.

автор: Karl J

28 окт. 2020 г.

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

автор: sven h

15 авг. 2020 г.

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

автор: Khursheda F

3 апр. 2020 г.

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

автор: Norbert J

17 авг. 2020 г.

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

автор: Francisco V A

17 нояб. 2020 г.

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.

автор: Alex H

19 авг. 2020 г.

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

автор: Giuseppe

9 янв. 2021 г.

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

автор: David M

31 дек. 2020 г.

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

автор: Edwin D R D

7 июня 2020 г.

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

автор: Bhavya J

26 июня 2020 г.

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

автор: Brian H

19 февр. 2020 г.

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

автор: Pedro B

24 авг. 2020 г.

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

автор: Clément p

6 нояб. 2021 г.

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

автор: Aayush T

11 июня 2020 г.

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

автор: Rui Z

31 июля 2020 г.

The lab session is not well instructed.

автор: Chow K M

21 июля 2021 г.

F​eedback on quiz can be improved.

автор: Ricardo A T L

25 авг. 2020 г.

Too General

автор: Angelo F

14 мая 2021 г.

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.

автор: Ruedi K

29 мар. 2020 г.

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.

автор: Ashish K

10 февр. 2022 г.

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.

автор: Rehan I

9 апр. 2020 г.

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!

автор: Tobias T

13 сент. 2020 г.

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.