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Comentários e feedback de alunos de Fundamentals of Machine Learning in Finance da instituição New York University

270 classificações
56 avaliações

Sobre o 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....

Melhores avaliações

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 Set 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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26 — 50 de 54 Avaliações para o Fundamentals of Machine Learning in Finance

por Wenxiao S

2 de Mar de 2020

The course is really challenging and requires a lot of self-motivated studying. I would say again it is the best course in quantitative finance that I have learned.

por Angelo J I T

10 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.

por Arditto T

3 de Set de 2019

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

por Siyu D

18 de Set de 2019

This is a great course, I strongly recommend. However, the assignments take a while to finish.

por Craig V

25 de Jul de 2020

Great class, but don't believe the programming assignment time estimates... takes way longer!

por Alvaro M

1 de Jan de 2020

Excellent course to get ML algorithms for profit maximization approach

por 刘晶

5 de Nov de 2018

It's excellent and incomparable course!

por Carlos S

7 de Abr de 2020

Great explanations and great material

por Yuning C

8 de Set de 2018

A great course with deep insight.

por Stefano M T

14 de Fev de 2020

Very interesting arguments!

por Pavel K

28 de Nov de 2018

Very informative

por Mohamed H a e r

8 de Dez de 2019

thanks coursera

por Serguei Z

25 de Jan de 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.

por Benny P

11 de Dez de 2019

For me, I find the math kind of useless. It's too hard for notice to understand, and too deep for those who don't want to know. This course should focus on its applications on finance. But at least you have few notebooks that you can keep for future reference.

por Hilmi E

5 de Ago de 2018

Good material..The course would improve a lot if there were clear explanations for the goals of the assignments and the plan for the assignment.. The codes for the assignment should be fully debugged..

por Jacques J

25 de Dez de 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.

por Aydar A

27 de Jun de 2019

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

por Bozanian K

19 de Ago de 2018

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

por gareth o

28 de Set de 2020

The course lecturing is good and having finance relevant examples is excellent but the programming exercises are very frustrating. The instructions are confusing and the final exercise requires a very long calculation that can time out. The forums are helpful though and it's all doable, a couple of tweaks and upgrading to Tensorflow2 would make this a 5* course

por cyril c

11 de Out de 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

por Harsh T

11 de Mar de 2020

Lectures assume that students know about Finance. For a person like me, all the finance terms are like jargon. Even though I have good knowledge of Machine Learning, the videos were difficult to follow. Not a very good amalgamation of Finance and Machine Learning.

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 Juan C G 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 Dez 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 Fev de 2019

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