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学生对 北方高等商学院 提供的 Python and Machine Learning for Asset Management 的评价和反馈

3.2
57 个评分
21 条评论

课程概述

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....
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1 - Python and Machine Learning for Asset Management 的 21 个评论(共 21 个)

创建者 Keith W

Nov 20, 2019

The jump in Python programming was not handled well - it was far too complex and an order of magnitude more complex than anything that had come before. I enjoyed the theory, but feel lost with the Python component. A 12 minute lab session with a Princeton grad student was not nearly enough to grasp the material. Bring back Vijay who is excellent in teaching Python!

创建者 Ziheng C

Dec 24, 2019

Personally, this is the BEST online course I have ever seen. For students with basic knowledge in machine learning and finance, this can help them improve a lot, especially helping them to combine these two things. In addition, the viewpoint of Professor John Mulvey is sharp and indicate directions for applying ML in investment management courses. Best course ever.

创建者 Andrea C

Jan 09, 2020

John part is really confusing and not well explained. his slides and very high level and labs are very low level with basically no explanation. The rest of the course is fine.

创建者 Soheil S

Jan 20, 2020

It was a terrible experience taking this course. Despite the two first courses, this one is disappointing! the ML instructor does not offer any useful material and all of the ML lectures contain ambiguous and useless material. The worst part is the quizzes. the multiple choices include ambiguous answers and that you should choose more than one and the ridiculous part is that either you would get the full mark or nothing! even if you choose some choices correctly and you never know what was your mistakenly chosen choice! I've tried the week 2 quiz for 9 times and have not been yet successful to pass it.

It's overwhelmingly complicated and unclear.

I didn't expect such a terrible course from EDHEC Business School and Coursera!

创建者 Serg D

Nov 23, 2019

Well, that was disappointing. What was the point bringing Princeton into this? Looks like edhec does not have in house ml experience. I did not find this course, exercises and labs to be practical at all. As another commentator said bring back Vijay!

创建者 Francisco C

Jan 17, 2020

I learned about how can be used the machine learning in asset management, but to much theory and nothing practical. We received the lab done, and could not understand how implement. I missed the lab of the first two courses.

创建者 Alex T

Mar 02, 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.

创建者 kitiwat a

Feb 06, 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.

创建者 Fabien N

Feb 01, 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.

创建者 Alexander D

Mar 01, 2020

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.

创建者 Brian H

Feb 19, 2020

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

创建者 Nicholas P D

Mar 15, 2020

The first two courses were very well done. This one is not even close to helpful. In the first two courses the Jupyter lab sessions were my favourite and really brought all the concepts together. The prof would go step by step through the code, even if it took an hour. In this course, I completely dread the lab sessions. They are only 15 minutes long and dump 200+ lines of uncommented code on you to deal with yourself. Also, it would be really nice if they could add presentation slides. All the lectures take twice as long because I have to pause and write down the formulas. It's sad because I used to look forward to learning, now I am just here to finish the specialization.

创建者 Jean-Luc B

Mar 07, 2020

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...

创建者 Loc N

Jan 02, 2020

The course feels chaotic and unplanned, unlike the previous two courses in the series. This course glosses over on some of the important technical details, while repeats too much basic or non-technical information. It also seems the course outsources the teaching to PhD students and readings, which causes further inconsistency.

创建者 Michinori K

Feb 13, 2020

This course is clearly of lower quality than the previous two courses of the Investment Management with Python and Machine Learning Specialization. Quiz is too ambiguous and very painful to pass.

创建者 Semant J P

Jan 13, 2020

First about me - I been deeply involved in data science, and machine learning and trade on the financial markets. So, in addition to solid academic credentials, I have a real life practical experience. I took this course to check if there were some additional skills I could learn.

I was sorely disappointed. This is a completely useless course.

The first two courses in this specialization were amazing. This has been the worst organized and least practical course. As other reviews have pointed out, academic research on regime filtering was pandered out as machine learning in finance. I was expecting to learn practical instances of using supervised, unsupervised, deep learning used in finance. There was nothing of this sort.

I have never seen Q-Q plots being used in investment/hedge funds - we talk about annualized returns, standard deviation, Sharpe ratio, and drawdowns. These statistical markers were used by Vijay in the first two courses. Not here.

This course needs to be rebuild from scratch - and Vijay needs to be brought back in for real practical application of ML in financial services.

创建者 Dirk W

Feb 05, 2020

Honestly, for this course, in the present state of work in progress, I can't give more than 1 star. Not well-constructed course, no right balance between theory and lab sessions. Theory on Machine Learning is on basic high-level concepts. Even the visual format of the lecture videos is irritating. Lab sessions are not always present, or not explained in a detailed manner, which is really a problem.

Stars are also missing because of a few frustrating quizzes and because of the lack of (quick/relevant) responses or answers of the moderators in the forum.

Please rework this course, with the high-quality other courses of this specialisation as example; please also take also the remarks of the students in the forum into consideration.

创建者 Xinhao

Mar 12, 2020

This is the worst course of this 4-courses specialization due to the useless lab-session. I miss VJ so badly....lol

创建者 Michelangelo D

Feb 19, 2020

1) poor explanations, few examples, bad audio quality... I was about to give up because of this 3rd course...

创建者 Alessandro F

Jan 30, 2020

Worst course in the all specialization! Bad lectures, and absolutely bad grading system.

Very disappointed!

创建者 Платонов Д Ю

Jan 03, 2020

Very bad. Previous 2 courses was amazing, but this is a strange.