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

뉴욕 대학교 공과 대학의 Guided Tour of Machine Learning in Finance 학습자 리뷰 및 피드백

3.7
310개의 평가
103 검토

강좌 소개

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....

최상위 리뷰

AB

May 28, 2018

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

SS

Mar 18, 2019

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

필터링 기준:

Guided Tour of Machine Learning in Finance의 95개 리뷰 중 1~25

By maciej.osinski

Dec 06, 2018

The lecture is actually good. The positive experience is totally ruined by the quality of programming assignments though. As someone put it on course forum - they seem as if someone built a poor implementation with odd design choices in rush, then deleted a couple of random lines and asked students to read his/her mind. Not sure if I'll continue the specialization now.

By Teemu P

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.

By Leo M

Dec 02, 2018

One of the worst courses I've taken on Coursera. These courses really need to be tested before put out for public consumption.

By 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

By Dawid L

Jan 27, 2019

Terrible. For the first time in long time I felt such abandoned. No support. Notebooks written sloppy with plenty of copy-paste and no fixing. Thought more of the lecturer as well but videos feel like he's just coming up with the material. Having strong mathematical background I felt that the lecturer is intentionally making simple things sound hard. I'm left with deep sense of wasted time. Leaving Coursera and never coming back.

By George D

Oct 24, 2018

interesting but big gap between lectures and coding assignments

By John G S

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.

By Denis K

Aug 21, 2018

1) I don't really understand who is the target audience for this course.

For those who already have experience with machine learning, there is very little new information related specifically to ML applications in finance, most of the course is just explaination of machine learning basics.

For those who are new machine learning, it is too brief and lacks explaination of practical aspects. I don't understand how someone with no ML experience is expected to do these buggy programming assigments with almost no guidance and little lecture materials explaining working with ML libraries.

If you are new to ML, there are many MUCH better courses available.

2) Programming assigments are terrible. There are critical bugs in code templates, bugs in evaluation, messy and unclean instructions. These problems are reported in forum discussions for months but still not fixed.

By Steven O

Aug 12, 2018

I would give this class zero stars if I could. It is a great topic and I had high expectations. The assignments are poorly worded, instructions are vague and that is putting it mildly. The material required to complete the assignments is mostly not covered in the lectures. I can't believe NYU gives its name to this jumbled mess. Buyer Beware!

By Minglu Z

Aug 05, 2018

The assignments are very bad. Some content are hard to understand what it wants me to do. So little instructions about the formula and model, on the contrary, it needs the EXACT SAME answer with the EXACT SAME process of the assignment wants to pass it.

The Quiz also very bad. ALL the questions are THE SAME AS the control questions in the videos.

Though the course has good content, I will not recommend anyone to take it.

By Bilal E

Jul 11, 2018

So many technical issues in the grading system. Also, Assignments are not clearly explained

By Christophe O

Apr 19, 2019

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

By Yi B

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.

By Ronald B

Mar 17, 2019

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

By Walter O A

Jan 05, 2019

I learned much and got good practice in Python and Tensorflow as well as good exposure to the literature. I was able to download the course materials from the course system and work out homework on my own system for which I was pleased. The automatic grading system worked without incident once I figured it out and did not crash on me. On the other hand, some of the homeworks were less than fully explained and/or motivated by the course material and did contain errors and omissions in the supplied code that I had to track down in order to get them correct. The feedback from the grader was of no use beyond stating whether the answer was correct, but this is pretty standard. The course was frustrating at times and I would recommend it only for students who are highly motivated, but for those who are, it is definitely worth the effort.

By Vladimir B

Aug 26, 2018

More or less this course is good and interesting. However, homework assignments were awful. It's unclear and it's very hard to understand what is asked and how it would be graded.

By Omar E O F

Jun 14, 2019

Very goo lectures, but assessment exercises are not well defined. Examples not shown in lectures. Not enough briefing for starting exercises. No active forum for discussion.

By 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).

By 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.

By Kiran

Jun 03, 2019

Good lectures

Irrelevant assignments

No help on forum

Don't take this as a paid course to pass

Just take this as an audit course

By 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)

By Amro T

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.

By Masato Y

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。

By Amir T

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.

By Swaminathan S

Mar 18, 2019

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