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Guided Tour of Machine Learning in Finance に戻る

New York University による Guided Tour of Machine Learning in Finance の受講者のレビューおよびフィードバック



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....




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.



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.


Guided Tour of Machine Learning in Finance: 126 - 150 / 193 レビュー

by T H


It is a very broad overview of the machine learning topics but very little about the applications in finance. It wont give you a foundation in machine learning nor any useful insights about financial applications...

by Wei X


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 Also TensorFlow 1.0 is out of date

by Philipp P


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.

by Maria A C G


Good Thing -> Problably the best explanations for gradient descent that I have ever seen.

Bad -> The exercises are very difficult for the level of explanations provided.

by Mike S


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.

by Vincent G


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)

by Vitalii A


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

by Yi W


The lecture is ok but lacks of details. The project is not well designed and hard to complete without much guidance.

by Alan X


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



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

by Jason X Z


There should be more explanations of codes in the video courses. Thanks.

by Manav A


Proper structure is absent but a lot of potential inside the course.

by Tom L


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

by Vicente I


It lacks information on how to proceed on NN coding.

by Masato Y



by Bhushan G



by Rudraroop R


I write this review as someone who came into this specialization with prior knowledge of ML and RL but not finance. For me there is more or less nothing new here. Only a few finance concepts sprinkled here and there. The lecture videos are good as a refresher to basic ML concepts but this is definitely not for someone with no prior knowledge of ML as the mathematics has not been dived into deep enough.

I​ had hoped that the assignments would be made in a way that guides you through the specifics of ML usage in the financial domain but they are very generic. The assignments and demos are written using outdated tensorflow code, they need to be updated. Moreover, for someone new to ML, completing these assignments would be next to impossible. The objectives are not clearly defined in the assignments and there is definitely not enough background covered here for someone to be able to jump over that hurdle without prior experience. Also there almost zero support from the course admins. Overall, not a very good course. The only positive is the instructor. Hopefully the other courses in the specialization are better than this.

by Tom G


The lectures and the concept for this course were very good. The problem was that it wasn't "guided" in any sense. There was a lot of time focusing on math concepts, but the way to apply those concepts in the code were glossed over or at times not even mentioned. The labs often asked you to do things that weren't covered at all in the lessons, forcing you to basically learn the coding through Googling. The forums weren't being monitored either, so if you felt like you were most of the way there but not getting the correct answer, there was no way to get a little guidance. Finally, the whole course was being taught on an older version of Tensorflow, and there are major differences between 1.x and 2.x, such that whatever I learn in this course I'll have to re-learn later if I want to operate in a current version of TF.

If you want to get the most out of this course, I recommend you come in with strong TF skills to begin with. I was going to take this whole specialization, but now I'm going to take an intro to TF class first and the reassess if I will continue or pick a different course set.

by Juraj S


The lectures that are present are useful. However, I feel like the course is broken with some of the videos missing, as the lecturer references topics/items from supposedly previous videos that were never mentioned (this occurs specifically in Week 4, where the section "Prediction of Earning per Share (EPS) with Scikit-learn and TensorFlow" only contains basic videos with an introduction to types of equity analysis and what fundamental analysis is, but there are no videos with actual Scikit-learn/Tensorflow examples).

The weekly quizzes are trivial - they just recycle the knowledge check questions from within the video, and as standalone questions often don't really make any sense. The programming assignments are very sparse on instructions or information of what is expected. So while students do get some hands-on experience implementing some things in sklearn and TensorFlow, for the majority of the time they're 'flying blind'.

by Amro T


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 Oliver P M


The course has rather decent videos, but the actual quality of exercises dunk after the very first one. Several exercises lack vital information in order to be able to successfully complete these without resorting to guesswork, while other pure and blatantly contains errors such as resetting the random number generator when taking new batches. In addition the solutions are so airtight, that rounding errors on the smallest of decimals causes one to get zero points, while the solution in any normal circumstance would be looked at as perfectly viable. Finally the version of tensorflow used is now so old, that the documentation has been scrapped from tensorflows own webpage, resulting in certain unexpected results whenever one tries to scoure the 1.15.0 documentation for an answer to certain problems.

by rfricks


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.

by Diego D


I believe that the course needs to improve the assignment piece. Instructions throughout the coding exercises are very poor. I understand that this course is for people with an intermediate level of python and Machine Learning knowledge, however because it promises to teach the practical applications of ML, some guidance it's needed. Even pointing out to a book as a reference for the algorithm would be enough. I completed the DeepLearning Specialization on Coursera and the quality of the teaching was way much higher.

by Alexander K


T​he assignments/labs are sometimes way different from lectures material. You may end up wasting hours and hours, googling and discussing the assignments on the forum before you finally understand it. A LOT of effort is required to complete the last labs because the lectures don't prepare you for the problem you're asked to solve. This is very disappointing.

by Jake K


Great theory. And good level of mathematical and statistical knowledge required to understand the concepts. However, It seems as though a lot of the coding aspect is brushed over and there is not much information given on how tensorflow works. Also, it needs updating to tensorflow version 2.