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Fundamentals of Machine Learning in Finance に戻る

New York University による Fundamentals of Machine Learning in Finance の受講者のレビューおよびフィードバック



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....




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



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


Fundamentals of Machine Learning in Finance: 26 - 50 / 66 レビュー

by Zoltan S


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.

by Daria


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!

by Tunan T


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!

by Wenxiao S


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.

by Angelo J I T


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

by Arditto T


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

by Siyu D


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

by Craig V


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

by Alvaro M


Excellent course to get ML algorithms for profit maximization approach

by 刘晶


It's excellent and incomparable course!

by Carlos S


Great explanations and great material

by Yuning C


A great course with deep insight.

by Stefano M T


Very interesting arguments!

by Pavel K


Very informative

by mohamed h


thanks coursera

by Sam


Thank you!

by Cannie L



by Benny P


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.

by Hilmi E


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..

by Jacques J


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.

by Aydar A


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

by Sergey M


I liked the course, but the bugs in the programming assignments are sometimes unbearable.

by Bozanian K


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

by Lorenzo B


Overall a good course, professor Halperin has a profound knowledge of the subject and he provides a lot of useful docs. Lectures are entirely focussed on theory, while the exams are based on Python coding, which is ok, however, the coding notebook are often buggy and the Week4 assignement is 'impossible' to pass without an external use of python as the Coursera cloud CPU is too low and code gets stuck in a infinite loop. I would suggest to Coursera to check and debug this.

by gareth o


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