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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

3.8
402개의 평가
123개의 리뷰

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

KD

Aug 24, 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

May 28, 2018

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

필터링 기준:

Guided Tour of Machine Learning in Finance의 110개 리뷰 중 1~25

교육 기관: 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.

교육 기관: 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.

교육 기관: 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.

교육 기관: 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.

교육 기관: B S C

Aug 22, 2018

Excellent course with some tech glitches that are being cleared up.

1) Outstanding lecturer in terms of both ML and Finance

2) Real substance to the course - e.g. I do ML in finance and have for some time, yet I found this "guided tour" to offer some real opportunities for thinking and working.

3) I think that compared to ML classes that use toy problems to illustrate ML algorithms, Prof Halperin sets up the problems so that students have to figure things out. This is an uncommon practice, and I welcome it, but not everybody will.

For example,there was an assignment involving censored regression that required students to actually do some research - like, searching google or Wikipedia to figure out the special characteristics of the regression problem being posed, and relate it back to the code. The kind of thing one might expect in a college course. This stands in contrast to spoon-fed projects and assignment that are common in MOOCs. This is unfortunately mistaken by many students for an accident (it did not help that there were some technical glitches with grading early on). It's still easy in terms of poblem-solving in contrast to many Quant MBA -tyype courses.

So, for people who want to get a Certificate that they know ML for Finance without doing much to earn it, this class may not be what they're looking for. Those who want to learn a bit, and do so under conditions intended to offer some features of real-world applications, will be rewarded.

교육 기관: 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

교육 기관: George D

Oct 24, 2018

interesting but big gap between lectures and coding assignments

교육 기관: 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.

교육 기관: Bilal E

Jul 11, 2018

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

교육 기관: 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.

교육 기관: 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!

교육 기관: 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.

교육 기관: 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.

교육 기관: 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.

교육 기관: Christophe O

Apr 19, 2019

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

교육 기관: 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.

교육 기관: 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

교육 기관: Ronald B

Mar 17, 2019

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

교육 기관: Pavel K

Nov 28, 2018

A very informative and well paced intro to ML / DL

교육 기관: GUILLERMO R J

Nov 29, 2018

Excellent Course, Professor clases are good complement for other ML courses.

교육 기관: Yangtao W

Dec 02, 2018

very good course!!!

교육 기관: Yergali B

Jan 04, 2019

Thank you, for this very useful course!

교육 기관: Juan A S

Jan 18, 2019

This course is a perfect introduction to machine learning applied to finance, which covers the essentialtopics that students must know to deepen their knowledge in this fascinating field.

교육 기관: Eduardo C

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.

교육 기관: Swaminathan S

Mar 18, 2019

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