Chevron Left
Вернуться к Guided Tour of Machine Learning in Finance

Отзывы учащихся о курсе Guided Tour of Machine Learning in Finance от партнера New York University

3.8
звезд
Оценки: 546
Рецензии: 172

О курсе

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....

Лучшие рецензии

KD
23 авг. 2019 г.

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 мая 2018 г.

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

Фильтр по:

76–100 из 159 отзывов о курсе Guided Tour of Machine Learning in Finance

автор: Nayan a

5 июня 2019 г.

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.

автор: Bozanian K

14 авг. 2018 г.

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

автор: Mihails S

1 янв. 2019 г.

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

автор: Xu Z

1 авг. 2018 г.

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.

автор: Maksim G

9 июня 2019 г.

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).

автор: Aydar A

24 мая 2019 г.

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)

автор: Hongsun K

18 янв. 2020 г.

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.

автор: Manimaran P

11 авг. 2018 г.

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

автор: Chad W L

12 июля 2018 г.

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.

автор: Ishrit T

16 июня 2019 г.

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

автор: Julien T

17 сент. 2018 г.

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

автор: Songjie H

3 июля 2020 г.

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

автор: Takayuki K

18 янв. 2019 г.

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

автор: Amalka W

12 сент. 2018 г.

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

автор: Zoraiz A

13 июля 2020 г.

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

автор: Rafael D d D

2 мая 2020 г.

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

автор: Zheng W

22 сент. 2018 г.

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

автор: Mohammed B

2 февр. 2020 г.

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

автор: gayatri l

7 февр. 2020 г.

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

автор: Edward W

26 июля 2020 г.

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

автор: Noordeen M

23 июня 2019 г.

was good but expect alitle explanation on the finance stuff

автор: Raphael R C

4 июля 2020 г.

Exercises need better explanations and code

автор: 徐晓彬

25 июня 2018 г.

The projects are not so understood.

автор: Wei-Chun K

27 апр. 2020 г.

The grading system isn't good.

автор: Alek R

17 окт. 2018 г.

Assignments were whack...