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Avis et commentaires pour d'étudiants pour Guided Tour of Machine Learning in Finance par New York University

3.8
étoiles
521 évaluations
162 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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101 - 125 sur 149 Avis pour Guided Tour of Machine Learning in Finance

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 Desi I

Sep 18, 2018

Good overview of ML and some basic applications to finance.

The pace is very good for people with some training in statistics and maths.

The assignments, however, are not particularly clear and with some obvious errors. There's room for improvement in the description of the exercises as well as including some tests to verify that you're getting the correct output.

par cyril c

Oct 11, 2018

content of the lessons is quite good, I would give it 5 stars if the assignments weren't so buggy, contains mistakes, unclear instructions, no help from staff/moderator/instructor, technical issues that are not resolved, etc. a lot of frustration, it just feels like the course was rushed to production and they let the students debug it

par Umendra C

Nov 18, 2018

Course material is good and a rating of 4 stars or more would have been a fair one, if it was not for very poorly designed and ill prepared assignments. The teaching staff really need to step up a level or two for the assignments.

The course content is good and that the only reason, I am still sticking with this specialization.

par Shobhit L

Aug 06, 2018

The assignments can improve a lot. The jupyter notebooks have no clarity in instructions and most of the time we have to struggle to find exactly what is expected from our code.

The specialization has a lot of potential, anchored only by the lack of the quality of the assignments.

par Curiosity2016

Sep 22, 2018

It's a good course but the homework is poorly designed with unclear instructions. Moreover, it's better to get familiar with Python before start this course. The suggested book "Hands-On Machine Learning with Scikit-Learn & TensorFlow" is a very good resource.

par Daham K

May 10, 2020

Great contents. Excellent topic.

But poor explanation especially in coding assignment.

The assignment includes every coding stuff you need to learn in this course. But there is no explanation about it. You can learn theory from prof. But...coding...?

par Wi K

Mar 08, 2020

The course content is a good review for machine learning with a preliminary introduction on TensorFlow 1.0. However the exercises are mediocre, without clear instruction. Also TensorFlow 1.0 is out of date

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 Mike S

Jan 04, 2020

The lectures were very good, but the assignments lacked supporting material. Also, most of the further reading was behind a paywall or the links had been removed.

par Vincent G

Nov 20, 2018

Content of the class is really good but technology/support is deplorable (Had to wait 3 weeks before the assignments got fixed by the support staff)

par Vitalii A

Dec 10, 2018

Not very related to finance plus most of the tasks are easy to complete, but hard to understand what needs to be done.

par Alan X

Jul 29, 2018

There is always something to be fixed in the assignments... Great content and relevance though.

par Gonzalo

Aug 31, 2018

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

par Lee H C T

Sep 23, 2018

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

par Vicente I

Dec 20, 2018

It lacks information on how to proceed on NN coding.

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 Bhushan G

Mar 19, 2020

D

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 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 Lee H

May 21, 2020

Not the best. If you are new to ML, there are much better courses out there, and the treatment here is too brief (I had done other courses on ML already, so it wasn't a show-stopper, but still I did not learn much here). The lecturer often speaks quickly with dense slides and barely enough time to read and digest everything on the screen before moving to the next. The assignments treat things not covered in the lectures and have many bugs. It's a shame as the content treated would be interesting to learn.

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 Ismael A C

Apr 16, 2020

The course approach very interesting subject. However, it has incomplete informations and guidance throughout chapeters. I've felt much more informed by the recommended literature: Hands-On Machine Learning with Scikit-Learn & TensorFlow, by Aurélien Géron.

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 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