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Learner Reviews & Feedback for Guided Tour of Machine Learning in Finance by New York University

3.8
stars
663 ratings

About the Course

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....

Top reviews

LP

Oct 22, 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.

KD

Aug 23, 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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126 - 150 of 206 Reviews for Guided Tour of Machine Learning in Finance

By Desi R 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.

By Dossiman

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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

By 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.

By Jean-François P

•

Sep 2, 2022

Having done Andrews Ng's course, the pedagogy of this course is not at the same level. The practical work is obscure and poorly guided. I had a hard time doing them knowing that I work in artificial intelligence and that I use these libraries on a daily basis. I am very hesitant to do the next 3 courses.

By Shobhit L

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Aug 5, 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.

By 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.

By 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...?

By Antony J

•

Dec 5, 2020

Very straightforward lectures followed by complex notebooks at a significantly higher level. Given that the labs are using a deprecated version of TensorFlow, with regret, I won't be pursuing this specialization any further.

By T H

•

Sep 3, 2020

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

•

Mar 7, 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

By Philipp P

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Oct 6, 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.

By Christopher W

•

Oct 11, 2023

Some of the labs/programming assignments were difficult to complete. There is not much guidance in some sections, and some python packages that were never demonstrated in the videos were used.

By Orestes S

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Jan 11, 2023

Lab exercises need improvements, as most of the time you need to guess what the real question is and they are not even covered in lectures!

Otherwise the course has lots of potential.

By Maria A C G

•

Nov 28, 2021

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

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Jan 4, 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.

By Vincent G

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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)

By Vitalii A

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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.

By Yi W

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May 10, 2022

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

By Alan X

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Jul 29, 2018

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

By GONZALO R

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Aug 31, 2018

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

By Jason X Z

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Feb 9, 2021

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

By Manav A

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Jul 12, 2020

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

By Victor N

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Feb 18, 2023

great content but horrible exercises with misleading instructions

By Tom L

•

Sep 23, 2018

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

By Vicente I

•

Dec 20, 2018

It lacks information on how to proceed on NN coding.