Chevron Left
Вернуться к Guided Tour of Machine Learning in Finance

Отзывы учащихся о курсе Guided Tour of Machine Learning in Finance от партнера New York University

3.8
звезд
Оценки: 545
Рецензии: 172

О курсе

This course aims at providing an introductory and broad overview of the field of ML with the focus on applications on Finance. Supervised Machine Learning methods are used in the capstone project to predict bank closures. Simultaneously, while this course can be taken as a separate course, it serves as a preview of topics that are covered in more details in subsequent modules of the specialization Machine Learning and Reinforcement Learning in Finance. The goal of Guided Tour of Machine Learning in Finance is to get a sense of what Machine Learning is, what it is for and in how many different financial problems it can be applied to. The course is designed for three categories of students: Practitioners working at financial institutions such as banks, asset management firms or hedge funds Individuals interested in applications of ML for personal day trading Current full-time students pursuing a degree in Finance, Statistics, Computer Science, Mathematics, Physics, Engineering or other related disciplines who want to learn about practical applications of ML in Finance Experience with Python (including numpy, pandas, and IPython/Jupyter notebooks), linear algebra, basic probability theory and basic calculus is necessary to complete assignments in this course....

Лучшие рецензии

KD
23 авг. 2019 г.

Introduction of ML for Financial application with combination of Scikit learn, Statsmodels and Tensorflow with neuralnets made this class very interesting. Learned and Enjoyed lot.

AB
27 мая 2018 г.

Exceptional disposition and lucid explanations! Ideal for a Risk Management professional to sharpen machine learning skills!

Фильтр по:

101–125 из 159 отзывов о курсе Guided Tour of Machine Learning in Finance

автор: Kevin C N

26 апр. 2020 г.

Great Course!

автор: Roland E

8 янв. 2020 г.

The assignments and project are very briefly explained. It took me a lot of unnecessary time to figure out what I was supposed to do. Also the discussion forum is inactive and I have a feeling many leave after seeing not anyone respond to their questions. I think there should be one or two dedicated support answering questions at least within 3 days.

The level of the course in general is pretty high, definitely not beginners level, which is fine I guess, but I do find the lectures are at times going very quick and at times overcomplicate. I would prefer an example to start simple and from there to build for a more complex situation. (For example start the bank failure with say 3 main features and show how you can decide to add another one by showing its impact through deviance and multicollinearity and show how you can then decide to add this new feature or not.)

автор: Fabien N

12 янв. 2020 г.

Actually I was finding that course amazing at first, but I gradually became very upset. The notebooks are way too high level and not self-explanatory. The teacher seems amazing by his knowledge, but one are left with the notebooks without knowing what to do, and the lectures only partially help to solve the problems. A lot of search online needs to be done and I don't think that is the spirit of Coursera courses. I was planning to pay for the whole specialization but unfortunately I will have to give up on this course that was very motivating at first...

автор: Luis S M

29 мар. 2020 г.

The lectures, as well as the quizzes, are great and coherent. However, the practical assignments, which are supposed to be the moment of cross-checking your level of comprehension of the learned topics are rather frustrating. I believe it would be of great help to future course takers to clearly state your expectations (e.g. through more detailed exercise descriptions) and introducing vital concepts before requiring their use.

автор: Ruixin Y

18 июня 2018 г.

Spent more time than expected. And when I tried to access the last assignment, it showed "404 : Not Found You are requesting a page that does not exist!"I understand the professor and other TA put a lot of effort on these courses, but I would say the assignments are not well organized, and more instructions are needed. Really hope the instructors could update/improve the courses/assignments. Thanks.

автор: Debasish K

26 февр. 2019 г.

Good because it gives a high level good overview of ML in Finance, SVM and Tensorflow.

However, Some examples are very easy and some have been made difficult by providing no references. Tobit regression was very vague. No links to proper reference. Neural Network was the example from Geron's Handbook but there were errors in the custom function that was defined.

More mathematical depth is required.

автор: Vincent L

25 авг. 2019 г.

extremely hard to follow, but better than when it originally came out. I had signed up after numerous ML courses and tried to skip to the later courses in this specialization. I got stuck trying to implement some crazy equations. I'm ok with looking up api methods, but the need to look out for reshaping is troublesome because it's inconsistent throughout the course. Overall, hard to follow.

автор: Desi I

18 сент. 2018 г.

Good overview of ML and some basic applications to finance.

The pace is very good for people with some training in statistics and maths.

The assignments, however, are not particularly clear and with some obvious errors. There's room for improvement in the description of the exercises as well as including some tests to verify that you're getting the correct output.

автор: cyril c

11 окт. 2018 г.

content of the lessons is quite good, I would give it 5 stars if the assignments weren't so buggy, contains mistakes, unclear instructions, no help from staff/moderator/instructor, technical issues that are not resolved, etc. a lot of frustration, it just feels like the course was rushed to production and they let the students debug it

автор: Umendra C

18 нояб. 2018 г.

Course material is good and a rating of 4 stars or more would have been a fair one, if it was not for very poorly designed and ill prepared assignments. The teaching staff really need to step up a level or two for the assignments.

The course content is good and that the only reason, I am still sticking with this specialization.

автор: Shobhit L

5 авг. 2018 г.

The assignments can improve a lot. The jupyter notebooks have no clarity in instructions and most of the time we have to struggle to find exactly what is expected from our code.

The specialization has a lot of potential, anchored only by the lack of the quality of the assignments.

автор: Curiosity2016

22 сент. 2018 г.

It's a good course but the homework is poorly designed with unclear instructions. Moreover, it's better to get familiar with Python before start this course. The suggested book "Hands-On Machine Learning with Scikit-Learn & TensorFlow" is a very good resource.

автор: Daham K

10 мая 2020 г.

Great contents. Excellent topic.

But poor explanation especially in coding assignment.

The assignment includes every coding stuff you need to learn in this course. But there is no explanation about it. You can learn theory from prof. But...coding...?

автор: Tianhao H

3 сент. 2020 г.

It is a very broad overview of the machine learning topics but very little about the applications in finance. It wont give you a foundation in machine learning nor any useful insights about financial applications...

автор: Wi K

7 мар. 2020 г.

The course content is a good review for machine learning with a preliminary introduction on TensorFlow 1.0. However the exercises are mediocre, without clear instruction. Also TensorFlow 1.0 is out of date

автор: Philipp P

6 окт. 2018 г.

Cons: overall content is good. Pros: when you release something (software or scientific article) you often do rigorous testing. Why not to do it with your Jupyter Notebooks? I do not understand it.

автор: Mike S

4 янв. 2020 г.

The lectures were very good, but the assignments lacked supporting material. Also, most of the further reading was behind a paywall or the links had been removed.

автор: Vincent G

20 нояб. 2018 г.

Content of the class is really good but technology/support is deplorable (Had to wait 3 weeks before the assignments got fixed by the support staff)

автор: Vitalii A

10 дек. 2018 г.

Not very related to finance plus most of the tasks are easy to complete, but hard to understand what needs to be done.

автор: Alan X

29 июля 2018 г.

There is always something to be fixed in the assignments... Great content and relevance though.

автор: Gonzalo

31 авг. 2018 г.

Great content, but the labs are difficult to understand and often unrelated with the content.

автор: Manav A

12 июля 2020 г.

Proper structure is absent but a lot of potential inside the course.

автор: Lee H C T

23 сент. 2018 г.

some python notebook has bugs, wasting time for me to fix

автор: Vicente I

20 дек. 2018 г.

It lacks information on how to proceed on NN coding.

автор: Masato Y

14 апр. 2019 г.

プログラミング課題でのプログラムの仕様がいまいちはっきりしない。