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Avis et commentaires pour d'étudiants pour Guided Tour of Machine Learning in Finance par Université de New York, Tandon School of Engineering

3.8
étoiles
453 évaluations
139 avis

À propos du cours

This course aims at providing an introductory and broad overview of the field of ML with the focus on applications on Finance. Supervised Machine Learning methods are used in the capstone project to predict bank closures. Simultaneously, while this course can be taken as a separate course, it serves as a preview of topics that are covered in more details in subsequent modules of the specialization Machine Learning and Reinforcement Learning in Finance. The goal of Guided Tour of Machine Learning in Finance is to get a sense of what Machine Learning is, what it is for and in how many different financial problems it can be applied to. 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

KD

Aug 24, 2019

Introduction of ML for Financial application with combination of Scikit learn, Statsmodels and Tensorflow with neuralnets made this class very interesting. Learned and Enjoyed lot.

AB

May 28, 2018

Exceptional disposition and lucid explanations! Ideal for a Risk Management professional to sharpen machine learning skills!

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51 - 75 sur 126 Avis pour Guided Tour of Machine Learning in Finance

par WangFangpo

Oct 07, 2019

很好的课程。推荐的论文很值得一读。

par Ezequiel A

Aug 07, 2018

Amazing Course!

par Vinay P K

Nov 20, 2018

good content

par hamid.zand

Jun 30, 2018

Great Course

par Russell H

Sep 01, 2018

Good overview of ML in Finance, clearly based on real-world experience. Would not recommend this as a first ML course; probably more useful after first taking another more general course, such as Guestrin's UW ML specialization. Some of the quizzes and exercises seem a bit rushed; e.g., out of order vs. the lectures and not clear about what is required. It was sometimes necessary to consult the discussion forums for clarification. The most useful part may be the categorization of ML algorithms along different axes, including applicability to different areas of finance. The readings and coding exercises seem to come mostly from Geron's O'Reilly book, so plan on buying that (it's a great book, so you should buy it whether you take this course or not).

par Benny P

Dec 06, 2019

This course has been informative, and extremely FUN! This is not to say that it's perfect, in fact as others say the assignments are quite challenging because there's little introduction to the problem/solution being asked. But that's exactly where the fun is! You need to search for the information yourself to solve the problem, much like in the real world. In fact I took another course on TensorFlow in the middle of this course to finish the assignment. But I can imagine this would be frustrating for those with less background on ML or programming, or people who expect everything to be presented smoothly for them.

par Hashim M

Dec 29, 2018

A much needed course by a very seasoned expert in the field, bringing the right blend of backgrounds in finance and tech. The course is well designed for finance professionals with some coding background and for technology professionals with some finance background - which is unique in that sense. Some bridging between lectures and assignments is needed but that kind of fine tuning is inevitable and as more students enroll, the discussion rooms and feedback will provide that sharpening at the edges organically. All in all, I enjoyed the course a lot and look forward to the next three in the specialization!

par Pedro M H V

Dec 06, 2018

Potentially great course with bridges technology (machine learning methods) and application (finance), but as for now it is really rough around the edges. Still needs to improve in terms of video lectures, resources and assignments; but once polished it could be a great course/specialization.

par Jose G H C

Sep 15, 2018

Um curso um que demanda um pouco mais que o usual, partindo desde o princípio de um ritmo rápido, com tarefas contendo explicações de somente o estritamente necessário. Entretanto, com uma temática muito interessante, e utilizando de várias técnicas.

par Philip T

Oct 04, 2018

Assignments are extremely difficult because the instructions are not clear. I understand that the act of working through the assignments is how you learn the material, however, this goes beyond that. It felt like a battle.

par Fred U

Feb 07, 2020

Great lectures. Homework is not trivial: it requires web searches and significantly more perseverance than, say, Andrew Ng's courses. Only 4 stars because I didn't see any recent signs of active support in the Forums.

par Nayan a

Jun 05, 2019

Lectures are mostly short review of the topic. So you should know topics beforehand or supplement it with readings. Problems are great, you cannot solve it unless you understand the concept properly, so that good point.

par Bozanian K

Aug 14, 2018

Very interesting course. Covers the main algorithms of supervised machine learning and their applications to the world of finance. The one and only down is that programming session are a little hard to understand

par Mihails S

Jan 01, 2019

Despite all the problems with the assignments and the grader this course provides really good overview ML tools and their application to finance. It's definitely worth the effort

par Xu Z

Aug 01, 2018

The course content is a mix of theory and practical stuff. One star off is due to the poor quality of programming assignment, i.e., unclear instructions and explanations.

par Maksim G

Jun 10, 2019

Good material but assignments explanation were too sparse and even expectation of material not covered in videos or readings (example is Tobit regression in week 4).

par Aydar A

May 24, 2019

To much math in lectures, assignments are not coherent and complicated, im not sure that i need tensorflow from scratch to work with finance(Keras fits better)

par Hongsun K

Jan 18, 2020

Great general overview of machine learning. I think the course can be re-organized to incorporate some of the theory and some coding tips as well, however.

par Manimaran P

Aug 11, 2018

The Lectures and given readings are very useful and it is required to read them to complete the assignments which will otherwise be difficult

par Chad W L

Jul 12, 2018

This will be a 5 star course when all of the technical issues are resolved. More timely feedback from the staff is desirable as well.

par Ishrit T

Jun 16, 2019

A more detailed introduction and guide to python for machine learning would have made this course one of the best out there

par Julien T

Sep 17, 2018

Very interesting content well delivered, the programming assignments could benefit from a little more guidance IMHO.

par Takayuki K

Jan 18, 2019

One of assignments was hard. Explanation by lecturer was very easy to understand and appropriate long.

par Amalka W

Sep 13, 2018

It would be great the background theory of related concept are explained in optional videos.

par Zheng W

Sep 22, 2018

The course content is okay, but the programming assignments are not well designed.