Chevron Left
Retour à Guided Tour of Machine Learning in Finance

Avis et commentaires pour d'étudiants pour Guided Tour of Machine Learning in Finance par New York University

3.8
étoiles
608 évaluations
195 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
23 août 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
27 mai 2018

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

Filtrer par :

76 - 100 sur 181 Avis pour Guided Tour of Machine Learning in Finance

par Fred U

7 févr. 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 Marina Z

22 avr. 2020

The course seems a bit of date (tensorflow) and 'lazy' -- assignments are sloppy, not related to the content of lectures sometimes, sometimes just replay of things form reading material... Promised more than delivered.

par Nayan a

5 juin 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

14 août 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 Jochen G

15 avr. 2021

Deep introduction into machine learning in finance. A bit outdated API-usage (Tensorflow 1), but nevertheless a great introduction for those who want to understand how the NN are processing the data.

par Mihails S

1 janv. 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 Zhiming X

1 août 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

9 juin 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

24 mai 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

18 janv. 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

11 août 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

12 juil. 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

16 juin 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

17 sept. 2018

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

par Kelly Y

17 avr. 2021

Great overview. Please provide more code examples as homework require a lot more than what the class covers!

par Songjie H

3 juil. 2020

Homework is not always consistent with what's covered in class. The recommended readings are very helpful.

par Takayuki K

18 janv. 2019

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

par Amalka W

12 sept. 2018

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

par Martin K

6 déc. 2020

It's a good course. There are some missing explanations in the programming exercises.

par Zoraiz A

13 juil. 2020

Later assignmnets were difficult but lecture material is interesting and well taught.

par Rafael D d D

2 mai 2020

Very good review and selected topics, although I would deep more on tensorflow use

par Zheng W

22 sept. 2018

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

par Mohammed B

2 févr. 2020

Great course, but the coding projects are sometime hard to understand

par gayatri l

7 févr. 2020

Learned ML concepts and algorithms to be used in financial work.

par Edward W

26 juil. 2020

Would be cool if was update to use latest version of tensorflow