Guided Tour of Machine Learning in Finance(으)로 돌아가기

3.7

(308개의 평가)

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....

대학: AB

•May 28, 2018

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

대학: SS

•Mar 18, 2019

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

필터링 기준:

90개의 리뷰

대학: Amro Tork

•May 19, 2019

This course is more of mathematical introduction to machine learning than actual practical machine learning tips and tricks course. Math is definitely crucial but the way it was conveyed was not really good. I would have provided a refresher week just in math to refresh the students before jumping into the mathematics in the course. In the notebooks, there is a lot that was missing. Because I was already familiar with the material and I used TensorFlow, Numpy, Sklearn and statsmodels before and built several models with them before, I was able to navigate through. But if I was a totally new student, I would have a very hard time going through those notebooks. A couple of good notes, Please try to summarize all the important equations into a PDF file either for the entire course or per week to be as a reference when needed.

대학: John G Schwitz

•Apr 23, 2019

I rate the lectures and the lecture material a 5; however, the exercises are poorly documented and prepared and there is zero presence on the Forums from any of the TA's. The exercises, Forum and lack of TA's I rate a 1. Thus the 3 rating.

대학: Christophe OLERON

•Apr 19, 2019

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

대학: 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.

대학: Masato Yonekawa

•Apr 14, 2019

プログラミング課題でのプログラムの仕様がいまいちはっきりしない。

대학: 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.

대학: Swaminathan Sethuraman

•Mar 18, 2019

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

대학: Ronald Bustamante

•Mar 17, 2019

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

대학: 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.

대학: 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.