Chevron Left
Retour à Guided Tour of Machine Learning in Finance

Avis et commentaires pour l'étudiant pour Guided Tour of Machine Learning in Finance par Université de New York, Tandon School of Engineering

3.8
378 notes
118 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!

Filtrer par :

51 - 75 sur 105 Examens pour Guided Tour of Machine Learning in Finance

par Bozanian K

Aug 14, 2018

Very interesting course. Covers the main algorithms of supervised machine learning and their applications to the world of finance. The one and only down is that programming session are a little hard to understand

par Russell H

Sep 01, 2018

Good overview of ML in Finance, clearly based on real-world experience. Would not recommend this as a first ML course; probably more useful after first taking another more general course, such as Guestrin's UW ML specialization. Some of the quizzes and exercises seem a bit rushed; e.g., out of order vs. the lectures and not clear about what is required. It was sometimes necessary to consult the discussion forums for clarification. The most useful part may be the categorization of ML algorithms along different axes, including applicability to different areas of finance. The readings and coding exercises seem to come mostly from Geron's O'Reilly book, so plan on buying that (it's a great book, so you should buy it whether you take this course or not).

par Alexander R

Oct 17, 2018

Assignments were whack...

par Amalka W

Sep 13, 2018

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

par 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.

par Julien T

Sep 17, 2018

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

par Zheng W

Sep 22, 2018

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

par 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.

par 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.

par 徐晓彬

Jun 25, 2018

The projects are not so understood.

par 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)

par 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.

par 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).

par 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

par Noordeen m

Jun 23, 2019

was good but expect alitle explanation on the finance stuff

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 Vicente I

Dec 20, 2018

It lacks information on how to proceed on NN coding.

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 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 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.

par Alan X

Jul 29, 2018

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

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 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 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 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.