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

3.7
170 Bewertungen
31 Bewertungen

Über den Kurs

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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26 - 30 of 30 Reviews for Fundamentals of Machine Learning in Finance

von 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.

von 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!

von Minglu Z

Aug 06, 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.

von 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

von 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.