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Opiniones y comentarios de aprendices correspondientes a Fundamentals of Machine Learning in Finance por parte de New York University

296 calificaciones
62 reseña

Acerca del Curso

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....

Principales reseñas

9 de ago. de 2019

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

2 de sep. de 2019

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

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51 - 60 de 60 revisiones para Fundamentals of Machine Learning in Finance

por Gerardo O

31 de mar. de 2020

Practical exercises are somehow disconnected from theory. Sometimes are not correctly guided and it is not clear the results they want to evaluate. Exercises can be done by navigating internet, the forums, but not reading the texts nor listening to the videos.

por Deleted A

5 de jun. de 2020

The programming assestment was uncorrelated to the content of the module, the main ideas are so great but thereis a problem connecting homework and content

por David C

19 de dic. de 2019

Good lectures, but the problem sets are difficult, contain errors, little guidance, and no mentor or staff available to help with problems.

por Lingzi

24 de feb. de 2019

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

por Mohammad A S

26 de mar. de 2020

Not practical. Mainly just some complicated math formulas.

por Rudraroop R

7 de jun. de 2021

U​tterly useless. Would give it zero stars if I could. The last assignment was just frustrating to complete, support is non existent, the lectures have little to do with the subsequent assignments, the assignments are outdated and teach you absolutely nothing about tensorflow as it is used today (read: who on earth doesn't use DENSE LAYERS in tf???). It's an absolute dump and I encourage you to stay away from this specialization altogether. Go do an Andrew Ng course instead if you want to learn something about Machine Learning. If you came into this with the object of expanding your ML knowledge into the financial realm and actually learning some finance along the way, then be assured that it won't help you accomplish anything. Hope I'm able to complete the specialization without smashing my computer in utter rage.

por Diego D

28 de abr. de 2021

Horrible course. It feels useless to follow it as the lectures are a bunch of topics that the instructor presents by giving superficial notions of them. The assignments barely relate to the lectures. Moreover the assignment notebooks are full of errors which makes hard to complete them. It seems that none care about this as the same issues have been highlighet by the students months after months and there is no support from the staff. I am disappointed in how Coursera has let the students down. Don't waste your money on this course.

por Chaofan S

19 de mar. de 2020

The assignment is not related to the contents and has bugs that no one responds.

por Arnav S

17 de mar. de 2020

Too bland. Reading off the slides. Couldn't understand anything.

por Ehsan F

27 de feb. de 2020

one of the worst courses I took in Coursera