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:

76–100 из 123 отзывов о курсе Python and Machine Learning for Asset Management

автор: Christopher B

28 мая 2020 г.

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.

автор: Jean-Luc B

7 мар. 2020 г.

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...

автор: Marco D

13 апр. 2020 г.

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

автор: NORIAKI S

26 сент. 2020 г.

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.

автор: Maximiliano M

6 мар. 2022 г.

The quality of the lab sessiones is really bad compared to previous modules. They are not explained properly and some important features were left aside or poorly taught such as coding structure. They tend to say "This is the way...". We are not MANDALORIANS... Another problem is related to the reading material, ie. week 5 reading list. It is not provided by the course and it's not available for free.

автор: Alex H

27 июля 2022 г.

There is a lot of interesting information here. However the jump from the lectures to the labs is gigantic. (And it's not the coding). The high-level explanations about ML concepts in the lectures were great, but the indepth breakdown of the models in the labs covered, what seems to be, hours more of material. I could not follow it.

автор: Loc N

2 янв. 2020 г.

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.

автор: Hilmi E

30 дек. 2020 г.

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..

автор: Jochen G

29 мая 2020 г.

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.

автор: Tim R

11 февр. 2022 г.

Repeats some of the concept of the first two courses of the specialization. Further, the Lab-session are a bit miserable. Compared to the first two courses the test are fairly straight forward and easy. In general, I did not nearly enjoy this course as much as the first two.

автор: Ilan J K L

18 мая 2020 г.

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.

автор: Marco K

22 июня 2020 г.

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.

автор: donald d

25 нояб. 2020 г.

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.

автор: Camilo R R

8 янв. 2022 г.

It doesn't teach you how to build the algorithm or the details of it and it ignores the good practice of the two previous courses of teaching you step by step. not recommended course.

автор: Daniel A C C

23 авг. 2020 г.

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.

автор: Toluwalope R

17 авг. 2020 г.

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

автор: Luis H C

15 нояб. 2020 г.

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

автор: Branson L J X

10 июля 2020 г.

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

автор: Samantha T

9 мая 2020 г.

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

автор: Nikolay A

13 мая 2020 г.

Not completely enough relevant information to pass Quises :(

автор: Fokrur R H

10 авг. 2020 г.

Worst course in the specialization

автор: Henry W

28 апр. 2020 г.

Professor Lionel is astute and insightful like he was in the first two courses. However the Machine Learning part taught by the other instructor and his PhD students is very lackluster; lacking explanations in both concepts and technicalities. The lab sessions and notebooks are poorly presented, libraries of codes are thrown without good explanation. The quiz questions are not covered by the content of the course, yet they are can be trivially answered, therefore the quiz completely fail to challenge the learners' understanding. As much as I liked the first two courses, I am afraid I cannot recommend this third course.

This course needs a complete rehaul, and NOT be taught by the same machine learning lecturer. Also the labs should preferably be taught in a similar style to Vijay. The combination of Lionel's insight and Vijays thoroughness is just too perfect. Its a shame Vijay cannot teach the 3rd course.

автор: Lucas F

26 апр. 2020 г.

The previous 2 modules were really good and I learnt a lot from both a theoretical and a practical point of view. Unfortunately, this was not the case on this one. There is significant room for improvement on both the structure and content of this module. A few issues:

The content is a bit confusing with a mix of what was taught on the previous two courses and new content. The quizzes are quite generic and don't cover the code given.

The intuition behind the statistical methods taught is just not there. You get the formulas but you wont really understand what is driving the methods. You don't get the economic intuition of the ML models applied to financial applications. I don't feel capable at all to use what was taught in outside applications.

Lab sessions lack quality and are not consistent with the previous two courses, unfortunately. A lot of space to improve here.

автор: Dinesh M

27 мар. 2021 г.

Compared to the other courses in this specialization, this course has very poorly organized materials especially when it comes to lab sessions and the pertinent resources. Quite unprofessionally, ineffectively organized resources, if I may say so to drive home the point. Because for most of the audience you are targetting via an online course: the following are most important: time efficiency. organization of materials, actual/real application vs just some theoretical familiarity. This course scores extremely low.

The quizzes are laughable at first, and annoying eventually. Extremely ambiguous questions and options; and very often during the quizzes as well as during labs/lectures unnecessary jargon is brought in.

Also annoying are the sections that are just repeats from the earlier modules.

автор: Tathagat K

29 мая 2020 г.

This is one of the worst MOOCS I've ever seen. I did ML by Andrew Ng without much background in the subject and was still able to follow and assimilate everything.

This MOOC is all about the prof and the students just showing you a haphazard, mixed up preview of what they know. They don't know anything about teaching, anything about explaining, anything about documentation and anything about framing questions for the quiz. The quiz sounds like something under-graduate teaching assistants have prepared by just looking at the videos without even understanding them.

And this MOOC is a massive contrast from the ones conducted by Vijay where he explains line by line, how to code the ideas that he teaches.

I'm thoroughly disappointed by EDHEC and Princeton.