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

628 évaluations

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


22 oct. 2021

Very useful course. Personally, I think that there should have been more focus on the implementation of tensorflow and neural network codes. Overall the course is well structured and very clear.


23 août 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.

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151 - 175 sur 193 Avis pour Guided Tour of Machine Learning in Finance

par Ismael A C

16 avr. 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 Baoye C

1 nov. 2020

The lectures are actually very good, but I think it would help tremendously if you can make the slides and sample Jupiter notebooks used in lecture available to us. It takes us a lot of time to recreate the notebooks just to play around with them.

par Lorenzo B

15 févr. 2022

Instructor is excellent but course is quite not optimally organized in documentation per each week, in relation to the requested topics on the assignments. Some topics in the questions are not covered in the explaination.

par Nicolás S

20 avr. 2021

The quality of the videos is bad, is hard to hear the lecturer. Also the programing assigments usually don't teach a lot, is usually write down two or three lines of code for a 4 part assignment.

par Hrishikesh A R

23 juin 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 Lakshmi P

4 août 2020

Please help me how can I submit my assignment , No submission script is active in my course as well as in my programming assignment . 6th august is my last date of my certified course .

par Andrew X

4 nov. 2021

Decent quality lectures, but programming assignments are only tangentially related to lecture content. Directions for programming assignments are very vague and generally lacking.

par Chris M

30 juin 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 Omar O

14 juin 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 Vivek U

14 juil. 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 Syed T H

11 avr. 2021

Material is insightful but the organization is not well structured. The instructor skips a lot and assumes that students will keep up on their own.

par Liuyi Y

16 mars 2020

I've practiced the project before and these projects are very messily written...I would suggest MIT 6.86 as an alternative for this intro course

par Conan H

27 sept. 2018

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

par Joao B

15 mars 2021

The grading system is broken and doesn't display any info in regards to what is wrong with your solution.

par Zicheng X

11 sept. 2018

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

par Abhinav C

16 févr. 2020

Was expecting bit more indepth. Very poor exercises with no reference to lectures. Disappointed.

par Simon N

1 déc. 2020

No feedback from tutor in forum. Exercises confusing without much value.

par Quentin V

29 juil. 2018

The automatic grading system does not work.

par Sean H

31 juil. 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 Mike B

10 mai 2021

This course is frustrating and should not be taken. This is due to errors that make it impossible to complete the course.

Currently (2021-05) the IPy notebooks have bugs that either need to be debugged (week 2) or outright settings errors that prevent them from being graded.

Putting aside the errors, the coverage of ML in finance is good, but not great. Too verbose with long explanations before any examples or visuals are given. The few visuals are just static equations too. Igor fails to write on the equations as he talks, making it hard to follow and (in the 1st several weeks) never references code, just the few equations.

Worse, the quizzes embedded in the videos are sometimes asking about info that's covered later in the same video ... but Coursera doesn't let you continue further in the video until passing the quiz. Plus, the content in the 1st three weeks uses TF 1 and has not been updated to use the TF 2 version in Coursera's current notebooks.

par Ngọc T

4 oct. 2018

Instructions completely unclear.Variables are named term1 and term2 with no reference to which formula. Not only is this not a unique decomposition (I could write this as 4 terms or 1 term depending on the algebra), but it is terrible coding practice.Covers material and requires knowledge of things never even discussed in the course. If this is done, it should be walked through pedagogically. This is for educational purposes after all. This assignment really seems like someone just wrote a jupyter notebook going through this calculation and erased a few random lines then expected us to be able to read their minds as to what was there.

par Ken H

8 mars 2022

Forums are dead. No one associated with the creation of the course has posted in years. Version of TensorFlow is outdated in the notebooks. If you want to work locally you will have to either find a way to install TensorFlow 1.x or port the notebooks to TensorFlow 2. The lab are frustrating but not because of the material. There are a lot version and notebook related issues that eat up your free time. I also don't like the way that the auto grader works. It is different from all other coursera course that I have taken. The difference is not helpful. The conent and assignments need to be updated for current best practices.

par Casey C

18 août 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.

par Nicolas B

3 avr. 2021









































, no clear instructions or explanations. (by the way, based on and old version of tensorflow with a different API). The exercises don't explain where the data comes from or what it represents or what output the grader expects (as in which variables your code is supposed to fill in). Wasted an amazing amount of time on trial and error instead of learning about finance or tensorflow.

The lectures seems to have been cut and pasted from another course: the speaker would say "welcome to week 2" on week 4, etc.

par Hussein H

25 janv. 2020

The name of the course is what caused me to purchase, I was super excited for this course up until i reached the coding assignment. The instructions we practically not succinct whatsoever and i literally had no clue what it was asking and how to even start. From the discussions and reviews it appears alot of people have this same issue.