Voltar para Guided Tour of Machine Learning in Finance

estrelas

592 classificações

•

191 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!

Filtrar por:

por Vincent G

•20 de Nov de 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)

por Vitalii A

•10 de Dez de 2018

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

por Alan X

•29 de Jul de 2018

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

por GONZALO R

•31 de Ago de 2018

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

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 Juraj S

•13 de Mai de 2021

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'.

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 Diego D

•21 de Mar de 2021

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.

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 Nicolás S

•20 de Abr de 2021

The quality of the videos is bad, is hard to hear the lecturer. Also the programing assigments usually don't teach a lot, is usually write down two or three lines of code for a 4 part assignment.

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.

- Como encontrar propósito e sentido na vida
- Compreendendo a pesquisa médica
- Japonês para iniciantes
- Introdução à computação em nuvem
- Fundamentos de Mindfulness
- Fundamentos de Finanças
- Aprendizagem Automática
- Aprendizagem automática usando o Sas Viya
- A ciência do bem-estar
- Rastreamento de Contato com a Covid-19
- IA para todos
- Mercados Financeiros
- Introdução à Psicologia
- Introdução à AWS
- Marketing internacional
- C++
- Análise Preditiva e Mineração de Dados
- Aprendendo a Aprender da UCSD
- Programação para todos da Universidade do Michigan
- Linguagem R da JHU
- Treinamento de CPI do Google CBRS

- Processamento da Linguagem Natural (PLN)
- IA para Medicina
- Bom com palavras: escrita e edição
- Modelagem de doenças infecciosas
- A pronúncia do inglês americano
- Automatização de teste de software
- Aprendizagem profunda
- Python para todosPython para todos
- Ciência de Dados
- Fundamentos de negóciosFundamentos dos Negócios
- Habilidades em Excel para negócios
- Ciência de Dados com Python
- Finanças para todos
- Habilidades de comunicação para engenheiros
- Treinamento de vendas
- Desenvolvimento e gestão de marca pessoal
- Análise de Dados de Negócios da Wharton
- Psicologia Positiva da Universidade da Pensilvânia
- Aprendizagem Automática da Universidade de Washington
- Design Gráfico da CalArts

- Certificados profissionais
- Certificados MasterTrack
- Suporte de TI do Google
- Ciência de dados da IBM
- Engenharia de Dados do Google Cloud
- IA aplicada da IBM
- Arquitetura do Google Cloud
- Analista de Cibersegurança da IBM
- Automação da TI do Google com Python
- Profissional de Mainframe do IBM z/OS
- Gestão aplicada de projetos da UCI
- Certificado em Design Instrucional
- Certificado em Engenharia e Gerenciamento de Construção
- Certificado de Big Data
- Certificado de Aprendizagem Automática em Análise de Dados
- Certificado em Gestão de Inovação e Empreendedorismo
- Certificado de Sustentabilidade e Desenvolvimento
- Certificado de Serviço Social
- Certificado de IA e Aprendizagem Automática

- Graduações em Ciência da Computação
- Graduações em Negócios
- Graduações em Saúde Pública
- Graduações em Ciência de Dados
- Bacharelados
- Bacharelado em Ciência da Computação
- Mestrado em Engenharia Elétrica
- Conclusão de bacharelado
- Mestrado em Gestão
- Mestrado em Ciência da Computação
- Mestrado em Saúde Pública
- Mestrado em Contabilidade
- Mestrado em Tecnologia da Computação e da Informação
- MBA On-line
- Mestrado em Ciência de Dados Aplicada
- MBA Global
- Mestrado em Inovação e Empreendedorismo
- Mestrado em Ciência de Dados
- Mestrado em Ciência da Computação
- Mestrado em saúde pública