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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

213 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


51 - Python and Machine Learning for Asset Management 的 75 個評論(共 94 個)

創建者 Pedro B


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

創建者 Aayush T


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

創建者 Rui Z


The lab session is not well instructed.

創建者 Ricardo A T L


Too General

創建者 kpb


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.

創建者 Ruediger K


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.

創建者 Rehan I


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!

創建者 Tobias T


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.

創建者 Christopher B


A lot of disjoint information about algorithms and finance was presented in a flashy way. Only about 10--20% of the course was genuinely about implementation of machine learning. All the code that was written was just thrown in front of you via pre-made note books without much explanation as to what was going on in terms of machine learning. Out of the four courses in this specialization, it was definitely the worst. Also, the assessments didn't really reflect the material that was covered at all. They were a struggle to pass without going back trying to dissect all the material.

創建者 Jean-Luc B


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...

創建者 Marco D


it ain't at the same level of the previous MOOC. There is no lab session for PCA/Clustering/Graphical Analysis that happens to be one of the most important topics for this MOOC; as a result, it should have been properly covered. Previous MOOCs are perfect, this one is not. Eventually, I would have expected this MOOC had spent more time going in details through coding part: lab sessions are not as effective as those of the previous MOOCs. I learned lots of useful techniques though, so it is worth in the end

創建者 Noriaki S


Slides and lectures (John's part) consists of ambiguous and high level remarks without concrete examples to help learners understand.

It would be better if we have the slides as files so that we don't have to scribble them. We cannot retain high level explanations in our mind by just listening and looking at the slides!

Quizzes were terrible. I wonder if the quizzes were prepared after checking the content of the lectures at all.

創建者 Loc N


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.

創建者 Hilmi E


This course lacks the quality of the first two courses of the series: presentations are poor, repetitive, sometimes trivial with unreadable visuals..Quizzes are childish at this level..

The labs contain good material but are poorly packaged(not fully debugged, multiple versions,unreadable video presentations) and presented..

創建者 Jochen G


Content is interesting, but course is poorly curated. Material provided (videos, readings and labs) are not fitting well to each other. One gets the feeling that essential parts of the slides were left out, references to past courses don't add up and exam questions are partially unanswered in the videos.

創建者 Ilan J K L


The course introduces you to some concepts in ML, however there is no audio from the lecturer in the end of the course, making it very tireing to finish. So far this is the weakest course of the specialization and I only finished it to complete the full specialization.

創建者 Marco K


poor explanations of the python sessions. Unlike first 2 MOOCS where I had the idea that I really learned while doing. Too many errors in coding. Plus set up of all kind of features without too much assistance. This course can be set up much better.

創建者 donald d


Interesting topics but now well put together. Much more theoretical than previous courses in specialization. Theory is fine but hard to adequately cover topics via 10 min videos. Quizzes were not very useful to learning the material.

創建者 Daniel A C C


Compared with the first to MOOCs this one is not so easy to understand since is most theory and the python lessons are given in 15 minutes with a huge of material to read.

創建者 Toluwalope R


It wasn't as good as the other courses. We didn't really get many useful lab sessions and opportunities to really understand the machine learning side in practice

創建者 Luis H C


Interesting content, but poorly explained. Significant drop in teaching quality compared to the first two courses of the specialization.

創建者 Branson L J X


Most of the time its just memory work. I didn't feel I learnt practical stuff, sorry.

創建者 Samantha T


The concepts are not explained clearly by the new team. Labs sessions were poor.

創建者 Nikolay A


Not completely enough relevant information to pass Quises :(

創建者 Fokrur R H


Worst course in the specialization