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Fundamentals of Machine Learning in Finance, New York University Tandon School of Engineering

3.5
83 個評分
19 個審閱

課程信息

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....
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18 個審閱

創建者 Pavel Konovalov

Nov 28, 2018

Very informative

創建者 Andreas Atle

Nov 21, 2018

Completely horrible labs.

And no response on the forums, errors in the labs remains for several months.

This is not acceptable, the course should be removed from Coursera!

創建者 刘晶

Nov 06, 2018

It's excellent and incomparable course!

創建者 Amalka Withana

Nov 01, 2018

If assignment are clear this course would be a great one. So I would like to suggest that explain more details about assignment and some guide lines

創建者 Philip Tabak

Oct 25, 2018

Many technical issues with assignments. Additionally, assignment instructions are often poor or insufficient.

創建者 Pierre Christophe Di Mayo

Oct 14, 2018

Not Worth the money. Although the assignments is a bit better than in the first course of the specialization, there is no help at all from the coursera team, even when it is impossible to grade the assignment. Do not spend your money there and buy some book instead

創建者 cyril clement

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

創建者 Dan Wang

Sep 25, 2018

The exercise doesn't match the course materials at all.

創建者 Yuning Chen

Sep 08, 2018

A great course with deep insight.

創建者 wasif.masood

Sep 08, 2018

This Prof. really have the talent of complicating even the most simplest of the ideas. His teaching method is really bad. Plus some assignments have nothing to do with that week's lectures.