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Avis et commentaires pour l'étudiant pour Guided Tour of Machine Learning in Finance par Université de New York, Tandon School of Engineering

3.8
378 notes
118 avis

À propos du cours

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....

Meilleurs avis

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!

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76 - 100 sur 105 Examens pour Guided Tour of Machine Learning in Finance

par Lee H C T

Sep 23, 2018

some python notebook has bugs, wasting time for me to fix

par Philipp P

Oct 06, 2018

Cons: overall content is good. Pros: when you release something (software or scientific article) you often do rigorous testing. Why not to do it with your Jupyter Notebooks? I do not understand it.

par Gonzalo

Aug 31, 2018

Great content, but the labs are difficult to understand and often unrelated with the content.

par Ruixin Y

Jun 18, 2018

Spent more time than expected. And when I tried to access the last assignment, it showed "404 : Not Found You are requesting a page that does not exist!"I understand the professor and other TA put a lot of effort on these courses, but I would say the assignments are not well organized, and more instructions are needed. Really hope the instructors could update/improve the courses/assignments. Thanks.

par 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。

par Vincent L

Aug 25, 2019

extremely hard to follow, but better than when it originally came out. I had signed up after numerous ML courses and tried to skip to the later courses in this specialization. I got stuck trying to implement some crazy equations. I'm ok with looking up api methods, but the need to look out for reshaping is troublesome because it's inconsistent throughout the course. Overall, hard to follow.

par Sridhar S

Sep 17, 2019

Good Lectures and Presentations. However, there are gaps in the theoretical explanations. The assignments and the Final Project requires considerable learning from the resources. Considerable portion of learning is achieved by completing them.

par Quentin V

Jul 29, 2018

The automatic grading system does not work.

par Ricardo F

Jul 22, 2018

I gave up while working on week 4's homework of the first course of this specialization. The two main reasons that led me to do so are: (1) very little on finance engineering except reference to problem cases and recommended readings; and (2) homework quality is really inferior to other machine learning courses I took at Coursera. I recognize that my first observation may not apply to the remaining courses of this specialization, but it is definitely the case in course 1. In the end, I thought I was not learning enough to justify the time and effort. Lectures are OK but they could be improved a lot by adding more financial engineering elements.

par Leo s

Sep 12, 2018

I faced some technique issue with submitting assignment. I hope there would be some technic help.

par Conan H

Sep 27, 2018

Interesting overview let down by lack of clarity on exercises such as the exact formulae and expected format of the outputs.

par Vivek U

Jul 14, 2018

Exellent content let down by endless flaws in grading system and lack of responses from tutor or instructor. Issues finally resolved 2 days before course end date.

par Chris M

Jul 01, 2018

Lectures are good, but assignments are half baked, under specified and half the grading has errors. I hope this improves for people that take (and pay for!) this in the future

par 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.

par Hrishikesh A R

Jun 23, 2019

Objectives of assignments are not clear. The instructions provided in assignments are not clear. Tensorflow should be taught extensively because most of the students are facing problems in same.

par 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.

par ALI R

Aug 19, 2019

The course material are presented sparsely despite my initial expectation which may be formed by Andrew Ng in his ML course. Anyway I believe it is a good roadmap for learners of ML in finance and also for me to find and I should be grateful of the Coursera.

par Andreas A

Nov 21, 2018

Horrible labs

par Deleted A

Jul 31, 2018

The course content is okay but the assignments are so poorly designed and no one responds to the questions on Week3 assignment #5.

par Sean H

Jul 31, 2018

The material is promising, but the staff running the course do not give a lot of direction on how to pursue learning the content. The programming assignments are left almost completely to the students guessing what they're suppose to do with little direction. There is almost no feedback on how your code has performed, except to say that your code was wrong, which you already understand from not getting the points. While I was able to achieve a passing grade in this course and the next, it was only because of the community of students that figured things out together, but with no other reliable way of figuring the material out. The code was also rife with bugs that weren't fixed for weeks while students tried and failed over and over again to pass assignments that they simply could not pass. It ended up wasting many hours of my time and, no doubt, other students' time. Simply check the forums to see the frustration from the Coursera community, that normally expects and receives high quality educational content.

par Ryland M

Aug 08, 2018

horrendous on all fronts. so disappointed. hoping the next course in specialization is better.

par Boris S

Aug 21, 2018

Totally useless course. The professor has no idea how to teach. I recommend to take a good course in machine learning and a good course in finance instead this one.

par wasif.masood

Sep 06, 2018

This guys uses so difficult language to explain which to me looks like as if he himself does not really know what he is teaching. This is really annoying. The course outline is good though.

par Pierre C D M

Oct 14, 2018

The assignements do not match the content of the video therefore you are not able to test whether you understood the material or not. Basically it is better to buy the book "Hands on machine Learning" by Geron and work on Financial exam

par Casey C

Aug 19, 2018

I am incredibly disappointed with this course. The subject material seems extremely interesting, and I couldn't wait to go through the course, but the graded programming assignments are terrible. They are vague to the point of impossible - the only way to pass them is to read the discussion forums and find a solution that has worked or guess and check. They cover material and techniques not even mentioned or referenced anywhere in the lectures or instructions. Worst of all, is these issues have been left unaddressed by the administrators for months despite students repeatedly voicing their concerns.