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.
Este curso faz parte do Programa de cursos integrados Machine Learning and Reinforcement Learning in Finance
oferecido por
Informações sobre o curso
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Resultados de carreira do aprendiz
50%
comecei uma nova carreira após concluir estes cursos
47%
consegui um benefício significativo de carreira com este curso
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário
Aprox. 24 horas para completar
Inglês
Legendas: Inglês, Francês
Resultados de carreira do aprendiz
50%
comecei uma nova carreira após concluir estes cursos
47%
consegui um benefício significativo de carreira com este curso
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário
Aprox. 24 horas para completar
Inglês
Legendas: Inglês, Francês
oferecido por
New York University
Tandon offers comprehensive courses in engineering, applied science and technology. Each course is rooted in a tradition of invention and entrepreneurship.
Programa - O que você aprenderá com este curso
3 horas para concluir
Artificial Intelligence & Machine Learning
3 horas para concluir
11 vídeos (Total 75 mín.), 3 leituras, 1 teste
11 videos
Welcome Note4min
Specialization Objectives8min
Specialization Prerequisites7min
Artificial Intelligence and Machine Learning, Part I6min
Artificial Intelligence and Machine Learning, Part II7min
Machine Learning as a Foundation of Artificial Intelligence, Part I5min
Machine Learning as a Foundation of Artificial Intelligence, Part II7min
Machine Learning as a Foundation of Artificial Intelligence, Part III7min
Machine Learning in Finance vs Machine Learning in Tech, Part I6min
Machine Learning in Finance vs Machine Learning in Tech, Part II6min
Machine Learning in Finance vs Machine Learning in Tech, Part III8min
3 leituras
The Business of Artificial Intelligence30min
How AI and Automation Will Shape Finance in the Future30min
A. Geron, “Hands-On Machine Learning with Scikit-Learn and TensorFlow”, Chapter 130min
1 exercício prático
Module 1 Quiz30min
6 horas para concluir
Mathematical Foundations of Machine Learning
6 horas para concluir
6 vídeos (Total 45 mín.), 3 leituras, 2 testes
6 videos
The No Free Lunch Theorem7min
Overfitting and Model Capacity8min
Linear Regression7min
Regularization, Validation Set, and Hyper-parameters10min
Overview of the Supervised Machine Learning in Finance3min
3 leituras
I. Goodfellow, Y. Bengio, A. Courville, “Deep Learning”, Chapters 4.5, 5.1, 5.2, 5.3, 5.41h
Leo Breiman, “Statistical Modeling: The Two Cultures”1h
Jupyter Notebook FAQ10min
1 exercício prático
Module 2 Quiz15min
6 horas para concluir
Introduction to Supervised Learning
6 horas para concluir
7 vídeos (Total 75 mín.), 4 leituras, 2 testes
7 videos
A First Demo of TensorFlow11min
Linear Regression in TensorFlow10min
Neural Networks11min
Gradient Descent Optimization10min
Gradient Descent for Neural Networks12min
Stochastic Gradient Descent8min
4 leituras
A.Geron, “Hands-On ML”, Chapter 9, Chapter 4 (Gradient Descent)1h
E. Fama and K. French, “Size and Book-to-Market Factors in Earnings and Returns”, Journal of Finance, vol. 50, no. 1 (1995), pp. 131-155.15min
J. Piotroski, “Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers”, Journal of Accounting Research, Vol. 38, Supplement: Studies on Accounting Information and the Economics of the Firm (2000), pp. 1-4115min
Jupyter Notebook FAQ10min
1 exercício prático
Module 3 Quiz15min
10 horas para concluir
Supervised Learning in Finance
10 horas para concluir
9 vídeos (Total 66 mín.), 4 leituras, 3 testes
9 videos
Fundamental Analysis7min
Machine Learning as Model Estimation8min
Maximum Likelihood Estimation10min
Probabilistic Classification Models6min
Logistic Regression for Modeling Bank Failures, Part I8min
Logistic Regression for Modeling Bank Failures, Part II5min
Logistic Regression for Modeling Bank Failures, Part III8min
Supervised Learning: Conclusion2min
4 leituras
C. Bishop, “Pattern Recognition and Machine Learning”, Chapters 4.1, 4.2, 4.31h
A. Geron, “Hands-On ML”, Chapters 3, Chapter 4 (Logistic Regression)1h
Jupyter Notebook FAQ10min
Jupyter Notebook FAQ10min
1 exercício prático
Module 4 Quiz21min
Avaliações
Principais avaliações do GUIDED TOUR OF MACHINE LEARNING IN FINANCE
por KD23 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.
por VB25 de Ago de 2018
More or less this course is good and interesting. However, homework assignments were awful. It's unclear and it's very hard to understand what is asked and how it would be graded.
por MS31 de Dez de 2018
Despite all the problems with the assignments and the grader this course provides really good overview ML tools and their application to finance. It's definitely worth the effort
por XZ31 de Jul de 2018
The course content is a mix of theory and practical stuff. One star off is due to the poor quality of programming assignment, i.e., unclear instructions and explanations.
Sobre Programa de cursos integrados Machine Learning and Reinforcement Learning in Finance
The main goal of this specialization is to provide the knowledge and practical skills necessary to develop a strong foundation on core paradigms and algorithms of machine learning (ML), with a particular focus on applications of ML to various practical problems in Finance.
The specialization aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include:
(1) mapping the problem on a general landscape of available ML methods,
(2) choosing particular ML approach(es) that would be most appropriate for resolving the problem, and
(3) successfully implementing a solution, and assessing its performance.
The specialization 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.
The modules can also be taken individually to improve relevant skills in a particular area of applications of ML to finance.
Perguntas Frequentes – FAQ
When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.
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