Chevron Left
Zurück zu Fundamentals of Machine Learning in Finance

Bewertung und Feedback des Lernenden für Fundamentals of Machine Learning in Finance von New York University

318 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....



9. Aug. 2019

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


2. Sep. 2019

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

Filtern nach:

26 - 50 von 71 Bewertungen für Fundamentals of Machine Learning in Finance

von Serg D

5. Dez. 2019

This course should be titled Machine learning algorithms and their formulas.

The course lectures are quite hard to follow all you see is formulas and little application to the finance. The only part of the finance is the dataset. No course materials. Really bad.

von Luis P

28. Jan. 2021

Horrible assignments. No help from TAs whatsoever. Zero finance explanation. And the machine learning content (tensorflow in particular) is outdated, nobody uses Tensorflow 1.0...

von Zoltan S

11. Aug. 2018

The lectures were truly outstanding, the best overview on different methods in machine learning I have seen so far. The problem sets were also interesting, informative and introduced several useful api from sklearn, tensorflow. With a little work these problem sets could (and probably should) be improved to match the quality of the lectures. For example adding more clarifications in the homework notebooks would be very helpful. Having said this, I think this is an excellent course, and highly recommend it.

von Daria Y

26. Okt. 2019

Great overview of main ML concepts with examples applicable to Finance. Even though some people might argue, that the videos don't provide a clear guide path to the assignments, I believe the course provides a simple explanation and great book references! Also, I supplemented my study with courses @DataCamp and other open sources - and it was quite beneficial as well. Thank you, Igor Halperin, & a team!

von Tunan T

28. Nov. 2020

This is a good starter course for people who wants to learn how to apply fundamental knowledge of machine learning into finance industry. Though the course is well designed, the lab assignment requires a bit effort to improve. There are some places the student will have no clue what goes wrong and how to resolve the issue. But overall a good course!

von Kenneth N

26. Juli 2022

Great course. but requires lot of patience. Uses lot of unnecessary history, symbols and equations to explain simple concepts. Overall it is a good overview of the big picture of ML in finance provided if u can withstand the assault of excessive symbols and equations.

von Wenxiao S

2. März 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.

von Angelo J I T

10. Aug. 2019

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

von Arditto T

3. Sep. 2019

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

von Siyu D

19. Sep. 2019

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

von Craig V

25. Juli 2020

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

von Alvaro M

1. Jan. 2020

Excellent course to get ML algorithms for profit maximization approach

von 刘晶

6. Nov. 2018

It's excellent and incomparable course!

von Carlos S

7. Apr. 2020

Great explanations and great material

von Yuning C

8. Sep. 2018

A great course with deep insight.

von Stefano M T

14. Feb. 2020

Very interesting arguments!

von Pavel K

28. Nov. 2018

Very informative

von mohamed h

8. Dez. 2019

thanks coursera

von Lam C V D

16. Nov. 2022

Slow labs 4

von Zhao Y

29. Sep. 2022

Thank you!

von Deleted A

31. Okt. 2021

Thank you!

von Cannie L

30. Dez. 2020


von Benny P

11. Dez. 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.

von Hilmi E

5. Aug. 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..

von Jacques J

25. Dez. 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.