Name: Fundamentals of Machine Learning in Finance
Brand: New York University
Availability: OnlineOnly
Rating: 3.7 (81 reviews)

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课程概述

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....

76 - Fundamentals of Machine Learning in Finance 的 83 个评论（共 83 个）

创建者 Amin D

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Dec 7, 2021

They did not spend 5 minutes on their assignment. You have to figure out the question and then find the answer by yourself. They do not teach what they ask for in assignment.

创建者 Pablo M

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Aug 2, 2024

too many bugs and problems with weekly tasks. Spend more time trying to solve problems than learning about them. The videos and explanations are very good and complete.