Voltar para Guided Tour of Machine Learning in Finance

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566 classificações

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180 avaliações

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....

KD

23 de Ago de 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

27 de Mai de 2018

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

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por Jason X Z

•9 de Fev de 2021

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

por Manav A

•12 de Jul de 2020

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

por Lee H C T

•23 de Set de 2018

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

por Vicente I

•20 de Dez de 2018

It lacks information on how to proceed on NN coding.

por Masato Y

•14 de Abr de 2019

プログラミング課題でのプログラムの仕様がいまいちはっきりしない。

por Bhushan G

•19 de Mar de 2020

D

por Amro T

•19 de Mai de 2019

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.

por Oliver P M

•14 de Jul de 2020

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.

por Ricardo F

•22 de Jul de 2018

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.

por Jake K

•7 de Dez de 2020

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.

por ALI R

•19 de Ago de 2019

The course material are presented sparsely despite my initial expectation which may be formed by Andrew Ng in his ML course. Anyway I believe it is a good roadmap for learners of ML in finance and also for me to find and I should be grateful of the Coursera.

por Ismael A C

•16 de Abr de 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.

por Baoye C

•1 de Nov de 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.

por Hrishikesh A R

•23 de Jun de 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.

por Lakshmi P

•4 de Ago de 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 .

por Chris M

•30 de Jun de 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

por Omar E O F

•14 de Jun de 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.

por Vivek U

•14 de Jul de 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.

por Liuyi Y

•16 de Mar de 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

por Conan H

•27 de Set de 2018

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

por Zicheng X

•11 de Set de 2018

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

por Abhinav C

•16 de Fev de 2020

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

por Simon N

•1 de Dez de 2020

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

por Quentin V

•29 de Jul de 2018

The automatic grading system does not work.

por Sean H

•31 de Jul de 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.

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