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Avis et commentaires pour d'étudiants pour Guided Tour of Machine Learning in Finance par Université de New York, Tandon School of Engineering

3.8
étoiles
455 évaluations
140 avis

À propos du cours

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....

Meilleurs avis

KD

Aug 24, 2019

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

May 28, 2018

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

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76 - 100 sur 127 Avis pour Guided Tour of Machine Learning in Finance

par Amalka W

Sep 13, 2018

It would be great the background theory of related concept are explained in optional videos.

par Zheng W

Sep 22, 2018

The course content is okay, but the programming assignments are not well designed.

par Mohammed B

Feb 02, 2020

Great course, but the coding projects are sometime hard to understand

par Gayatri G L

Feb 07, 2020

Learned ML concepts and algorithms to be used in financial work.

par Noordeen m

Jun 23, 2019

was good but expect alitle explanation on the finance stuff

par 徐晓彬

Jun 25, 2018

The projects are not so understood.

par Alexander R

Oct 17, 2018

Assignments were whack...

par Roland E

Jan 09, 2020

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.)

par Fabien N

Jan 12, 2020

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...

par Ruixin Y

Jun 18, 2018

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.

par Debasish K

Feb 26, 2019

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.

par Vincent L

Aug 25, 2019

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.

par Desi I

Sep 18, 2018

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.

par cyril c

Oct 11, 2018

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

par Umendra C

Nov 18, 2018

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.

par Shobhit L

Aug 06, 2018

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.

par Curiosity2016

Sep 22, 2018

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.

par Philipp P

Oct 06, 2018

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.

par Mike S

Jan 04, 2020

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.

par Vincent G

Nov 20, 2018

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)

par Vitalii A

Dec 10, 2018

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

par Alan X

Jul 29, 2018

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

par Gonzalo

Aug 31, 2018

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

par Lee H C T

Sep 23, 2018

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

par Vicente I

Dec 20, 2018

It lacks information on how to proceed on NN coding.