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Volver a Guided Tour of Machine Learning in Finance

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

285 calificaciones
99 revisiones

Acerca de este Curso

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....

Principales revisiones

por AB

May 28, 2018

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

por SS

Mar 18, 2019

Excellent. I picked up quite a bit of ML as applied to finance through this fast paced course.

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88 revisiones

por Christophe OLERON

Apr 19, 2019

Very Difficult - Impossible to succeed without very strong prior experience. Would deserve more guidelines

por Yi Bao

Apr 15, 2019

The course is not mature enough. If someone wants to learn machine learning in finance with efficiency and practicality, he or she should consider other options instead of this specialization/course.

por Masato Yonekawa

Apr 14, 2019


por Amir Tavakoli-Kashi

Apr 12, 2019

The teaching quality is poor and lacks practical examples. It is too technical, which you don't expect for this kind of courses. The mathematics were presented poorly and sometimes without context.

por Swaminathan Sethuraman

Mar 18, 2019

Excellent. I picked up quite a bit of ML as applied to finance through this fast paced course.

por Ronald Bustamante

Mar 17, 2019

The assignments of the last week were poorly planned, almost impossible to understand.

por Eduardo Chemalle

Mar 05, 2019

Excellent! it is very wider and get to be so clear at the same time. It was an amazing experience specially because I am returning back to Coursera courses.

por Debasish Kundu

Feb 26, 2019

Good because it gives a high level good overview of ML in Finance, SVM and Tensorflow.

However, Some examples are very easy and some have been made difficult by providing no references. Tobit regression was very vague. No links to proper reference. Neural Network was the example from Geron's Handbook but there were errors in the custom function that was defined.

More mathematical depth is required.

por lcy9086

Feb 25, 2019

Not an introductory level course. If you are new to machine learning, I would suggest taking Andrew Ng's course.....However some materials in this course are somewhat deep and rewarding if you have already got the basis..

The programming assignment is somehow painful and literally no introduction and demonstration of tensorflow is provided..... You need to do the reading and search the forum to get help to do the assignment

por Teemu Puutio

Feb 24, 2019

Do not take this course before you review week 2,3 and 4 coding assignments which are wholly disconnected and arbitrary guesswork assignments where your task is to fill in missing pieces of code without any guidance or support. In its current stage the course is inaccessible to all but most tenacious learners with significant python and scikit experience.