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Learner Reviews & Feedback for Fundamentals of Machine Learning in Finance by New York University Tandon School of Engineering

3.7
149 ratings
28 reviews

About the Course

The course aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include: (1) understanding where the problem one faces lands on a general landscape of available ML methods, (2) understanding which particular ML approach(es) would be most appropriate for resolving the problem, and (3) ability to successfully implement a solution, and assess its performance. A learner with some or no previous knowledge of Machine Learning (ML) will get to know main algorithms of Supervised and Unsupervised Learning, and Reinforcement Learning, and will be able to use ML open source Python packages to design, test, and implement ML algorithms in Finance. Fundamentals of Machine Learning in Finance will provide more at-depth view of supervised, unsupervised, and reinforcement learning, and end up in a project on using unsupervised learning for implementing a simple portfolio trading strategy. 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....
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1 - 25 of 29 Reviews for Fundamentals of Machine Learning in Finance

By Omar E O F

Jul 01, 2019

Not enough support from the staff. The assignments are strange, some don't relate to the lectures, some are hard to identify what kind of answer is being expected from us. Discussion forums are a joke, I've managed to go through because of some good souls like Kurt Woschnagg. Great lectures though, content is very nice, but not enough visualizations of the math presented.

By Aydar A

Jun 28, 2019

Good course with relevant topics, but assignments are not clear sometimes, lack of support with them.

By Nicolas M

Apr 01, 2019

good overview of methods but project part was frustrating due to slow Jupyter servers which blocked progress. Overall still positive as course content is unique.

By Ronald B

Mar 31, 2019

Assignments are poorly designed. Staff is unresponsive. IThe same appened with the first course of the specialization.

By Teemu P

Mar 03, 2019

Do not attempt this course unless you are extremely experienced in the topic and python already.

By Lingzi

Feb 24, 2019

the course content is okay. but the coding exam really needs improvement.

By Umendra C

Feb 02, 2019

This could have been the real deal with so many fascinating topics to learn here, but unfortunately, this specialization is setting new low standards in each assignments. The grader does not work, sometime we are asked to produce wrong results (as oppose to the research material). It is very frustrating!

Good reading assignments.

They need better and more qualified support staff.

By Daniel F

Jan 13, 2019

Content is good but assignments are buggy.

By Pramanshu R

Jan 08, 2019

Content and programming assignments are not much correlated. Lots of kernel problems while submitting assignments and late reply by staff.

By Luis A A C

Jan 07, 2019

Excellent course.

I only wish to have had programming assignment with RNN and Hidden Markov Models instead of three assignments on PCA. Although they highlighted a interesting application in finance.

By Jacques J

Dec 25, 2018

So far so good. The lecturer refers to projects of which some weren't covered in this course. So a little confusing. Takes lots of googling to finish this course.

By Pavel K

Nov 28, 2018

Very informative

By Andreas A

Nov 21, 2018

Completely horrible labs.

And no response on the forums, errors in the labs remains for several months.

This is not acceptable, the course should be removed from Coursera!

By 刘晶

Nov 06, 2018

It's excellent and incomparable course!

By Amalka W

Nov 01, 2018

If assignment are clear this course would be a great one. So I would like to suggest that explain more details about assignment and some guide lines

By Philip T

Oct 25, 2018

Many technical issues with assignments. Additionally, assignment instructions are often poor or insufficient.

By Pierre C D M

Oct 14, 2018

Not Worth the money. Although the assignments is a bit better than in the first course of the specialization, there is no help at all from the coursera team, even when it is impossible to grade the assignment. Do not spend your money there and buy some book instead

By 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

By Dan W

Sep 25, 2018

The exercise doesn't match the course materials at all.

By Yuning C

Sep 08, 2018

A great course with deep insight.

By wasif.masood

Sep 08, 2018

This Prof. really have the talent of complicating even the most simplest of the ideas. His teaching method is really bad. Plus some assignments have nothing to do with that week's lectures.

By tze s

Sep 02, 2018

WORST CLASS EVER. Stay away!!!! I want my money back. (and even that is not possible).

the homework autograder does not work. The mentors tell you to simply upload code of which everybody knows that it is incorrect instead of fixing the autograder.

Sometimes those incorrect "fixes" that the mentors give, don't work either. So no way of finishing the class.

Audio of the videos is of very poor quality.

By Matthieu B

Aug 31, 2018

Too many shortcomings and errors assessments. Tests at the end of the videos cut what Igor is saying and they are often about the following video.

The assessments are also very shallow compared to what we are supposed to learn and the 10-people staff is never online and almost never answers any message.

By Casey C

Aug 29, 2018

Assignments are atrocious, replete with errors. Staff seems not to care as these have been pointed out and left unfixed for months.

By Bozanian K

Aug 19, 2018

Add some hints in the notebooks, it was very hard to understand some parts