Chevron Left
Back to Fundamentals of Machine Learning in Finance

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

321 ratings

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....

Top reviews


Aug 9, 2019

Furthered my understanding of how probabilistic models are connected to Machine Learning models. Very happy with the content in this course.


Sep 2, 2019

Great course which covers both theories as well as practical skills in the real implementations in the financial world.

Filter by:

1 - 25 of 72 Reviews for Fundamentals of Machine Learning in Finance

By Sean H

Jul 31, 2018

The material is promising, but the staff running the course do not give a lot of direction on how to pursue learning the content. On the other hand, there is a lot of repeated material from the previous course. I do not know if they expect students to jump in at different parts of the specialization, but it seemed unnecessary. The rest of this review is a repeat of my review for the previous course, but still holds true in this course. The programming assignments are left almost completely to the students guessing what they're suppose to do with little direction. There is almost no feedback on how your code has performed, except to say that your code was wrong, which you already understand from not getting the points. While I was able to achieve a passing grade in this course, it was only because of the community of students that figured things out together, but with no other reliable way of figuring the material out. The code was also rife with bugs that weren't fixed for weeks while students tried and failed over and over again to pass assignments that they simply could not pass. It ended up wasting many hours of my time and, no doubt, other students' time. Simply check the forums to see the frustration from the Coursera community, that normally expects and receives high quality educational content.

By wasif.masood

Sep 8, 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 Andreas A

Nov 20, 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 Ronald B

Mar 31, 2019

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

By tze s

Sep 1, 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 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 Pramanshu R

Jan 8, 2019

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

By Daniel F

Jan 12, 2019

Content is good but assignments are buggy.

By Alexander K

Jun 12, 2022

The course assignments are an absolute disgrace and disrespect of students. Just a few examples:

- The tasks use old and gray tools and don't get updated for years. How do you like 4-year old TensorFlow 1.10.1?

- The course project has a task where you need to train a few TF neural nets. This takes HOURS to run in the provided Jupyter environment. Are you crazy there? In order to solve it I had to download everything manually, replicate the old-as-my-granddad environement and run locally. This took me 3 hours that I wish I could get back.

- Staff doesn't pay any attention to students' complaints. Just have a look at the discussion forum and how may times they replied. Once in 4 years?

- Be ready to google and investigate A LOT if you plan to do the assignments. The information you'll be given is arguably < 30% of what you'll need.

By Nicolas S

Jan 2, 2020

The content of this course is not suited for online training. The videos mostly present an overview of the ML equations. You will have to consult textbooks for a deeper understanding. The exercises lack of guidance and do not have any conclusion. You will have a hard time to complete all the assignements if you don't know already well Scikit-learn, pandas, or even tensorflow.

By Minglu Z

Aug 6, 2018

The assignment submitting problem is fixed. But the confusing requirements are still in assignments. Always be stuck by concept or formula which irrelevant to the ML.

Not recommend.

By Teemu P

Mar 3, 2019

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

By Dan W

Sep 25, 2018

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

By Omar O

Jul 1, 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 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 Thomas W

Feb 17, 2021

The video lectures are quite nice although I miss the promised link to finance at time. What is absolutely a disaster are the assignments. They are flawed and miss a lot of explanation. A sometimes I miss the connection to the lectures. I will not recommend the course.

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 Luis A

Jan 7, 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 Serguei Z

Jan 25, 2020

The course offers a good review of techniques. The coding assignments can be improved, in my opinion. On the one hand, they are quite simplistic and do not require understanding of the course material, the algorithms or the theory to be completed - one just needs to mechanically follow the code and write appropriate lines. On the other hand, the grading algorithms are sometime stuck on technicalities that are not relevant either understanding or programming but may require significant time to figure out the correct solution.

By Lorenzo B

Mar 5, 2022

Overall a good course, professor Halperin has a profound knowledge of the subject and he provides a lot of useful docs. Lectures are entirely focussed on theory, while the exams are based on Python coding, which is ok, however, the coding notebook are often buggy and the Week4 assignement is 'impossible' to pass without an external use of python as the Coursera cloud CPU is too low and code gets stuck in a infinite loop. I would suggest to Coursera to check and debug this.

By Umendra C

Feb 2, 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 Nicolas M

Apr 1, 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 Amalka W

Nov 1, 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 Sung-Yeon L (

Jan 19, 2020

The course staff should prepare more (the assignment and the support). Seriously. Too much ambiguities in the assignments, and discrepancies between the lectures and assignments.

I personally liked the material, especially that the course tried to deliver some real life applications.