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

3.8
355 Bewertungen
112 Bewertungen

Über den Kurs

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-Bewertungen

AB

May 28, 2018

Exceptional disposition and lucid explanations! Ideal for a Risk Management professional to sharpen machine learning skills!

SS

Mar 18, 2019

Excellent. I picked up quite a bit of ML as applied to finance through this fast paced course.

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51 - 75 of 100 Reviews for Guided Tour of Machine Learning in Finance

von Alexander R

Oct 17, 2018

Assignments were whack...

von Amalka W

Sep 13, 2018

It would be great the background theory of related concept are explained in optional videos.

von Jose G H C

Sep 15, 2018

Um curso um que demanda um pouco mais que o usual, partindo desde o princípio de um ritmo rápido, com tarefas contendo explicações de somente o estritamente necessário. Entretanto, com uma temática muito interessante, e utilizando de várias técnicas.

von Julien T

Sep 17, 2018

Very interesting content well delivered, the programming assignments could benefit from a little more guidance IMHO.

von Zheng W

Sep 22, 2018

The course content is okay, but the programming assignments are not well designed.

von Philip T

Oct 04, 2018

Assignments are extremely difficult because the instructions are not clear. I understand that the act of working through the assignments is how you learn the material, however, this goes beyond that. It felt like a battle.

von Chad W L

Jul 12, 2018

This will be a 5 star course when all of the technical issues are resolved. More timely feedback from the staff is desirable as well.

von 徐晓彬

Jun 25, 2018

The projects are not so understood.

von Aydar A

May 24, 2019

To much math in lectures, assignments are not coherent and complicated, im not sure that i need tensorflow from scratch to work with finance(Keras fits better)

von Nayan a

Jun 05, 2019

Lectures are mostly short review of the topic. So you should know topics beforehand or supplement it with readings. Problems are great, you cannot solve it unless you understand the concept properly, so that good point.

von Maksim G

Jun 10, 2019

Good material but assignments explanation were too sparse and even expectation of material not covered in videos or readings (example is Tobit regression in week 4).

von Ishrit T

Jun 16, 2019

A more detailed introduction and guide to python for machine learning would have made this course one of the best out there

von Noordeen m

Jun 23, 2019

was good but expect alitle explanation on the finance stuff

von 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.

von Vicente I

Dec 20, 2018

It lacks information on how to proceed on NN coding.

von 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.

von 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)

von Debasish K

Feb 26, 2019

Good because it gives a high level good overview of ML in Finance, SVM and Tensorflow.

However, Some examples are very easy and some have been made difficult by providing no references. Tobit regression was very vague. No links to proper reference. Neural Network was the example from Geron's Handbook but there were errors in the custom function that was defined.

More mathematical depth is required.

von Alan X

Jul 29, 2018

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

von 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.

von 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

von 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.

von 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.

von Lee H C T

Sep 23, 2018

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

von 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.