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Avis et commentaires pour l'étudiant pour Fundamentals of Machine Learning in Finance par Université de New York, Tandon School of Engineering

3.8
192 notes
34 avis

À propos du cours

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....

Meilleurs avis

AT

Aug 10, 2019

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

AT

Sep 03, 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 - 33 sur 33 Examens pour Fundamentals of Machine Learning in Finance

par Arditto T

Sep 03, 2019

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

par Siyu D

Sep 19, 2019

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

par Daria

Oct 26, 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!

par Jacques J

Dec 25, 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.

par Bozanian K

Aug 19, 2018

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

par Hilmi E

Aug 05, 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..

par Aydar A

Jun 28, 2019

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

par cyril c

Oct 11, 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