Acerca de este Curso

26,092 vistas recientes

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Nivel intermedio

Aprox. 20 horas para completar

Sugerido: 5 weeks - 2/3 hours per week...

Inglés (English)

Subtítulos: Inglés (English)

Qué aprenderás

  • Check

    Learn the principles of supervised and unsupervised machine learning techniques to financial data sets

  • Check

    Understand the basis of logistical regression and ML algorithms for classifying variables into one of two outcomes

  • Check

    Utilize powerful Python libraries to implement machine learning algorithms in case studies

  • Check

    Learn about factor models and regime switching models and their use in investment management

Habilidades que obtendrás

Programming skillsManaging your own personal invetsmentsInvestment management knowledgeComputer ScienceExpertise in data science

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Nivel intermedio

Aprox. 20 horas para completar

Sugerido: 5 weeks - 2/3 hours per week...

Inglés (English)

Subtítulos: Inglés (English)

Instructores

Imagen del instructor, John Mulvey - Princeton University

John Mulvey - Princeton University 

Professor in the Operations Research and Financial Engineering Department and a founding member of the Bendheim Centre for Finance at Princeton University
Finance
1,869 alumnos
1 curso
Imagen del instructor, Lionel Martellini, PhD

Lionel Martellini, PhD 

EDHEC-Risk Institute, Director
Finance
6,205 alumnos
3 cursos

ofrecido por

Logotipo de Escuela de Negocios EDHEC

Escuela de Negocios EDHEC

Programa - Qué aprenderás en este curso

Semana
1

Semana 1

2 horas para completar

Introducing the fundamentals of machine learning

2 horas para completar
8 videos (Total 59 minutos), 4 lecturas, 1 cuestionario
8 videos
Introduction to machine-learning7m
Financial applications7m
Supervised learning7m
First algorithms7m
Highlights of best practice6m
Unsupervised learning7m
Challenges ahead10m
4 lecturas
Requirements2m
Material at your disposal2m
Machine Learning for Investment Decisions: A Brief Guided Tour10m
References for module 1"Introducing the fundamentals of machine learning"10m
1 ejercicio de práctica
Module 1Graded Quiz30m
Semana
2

Semana 2

4 horas para completar

Machine learning techniques for robust estimation of factor models

4 horas para completar
8 videos (Total 80 minutos), 2 lecturas, 1 cuestionario
8 videos
Introducing Factor Models7m
Typology of factor models9m
Using factor models in portfolio construction and analysis10m
Penalty methods9m
Setting factor loadings and examples7m
Shrinkage concepts7m
Lab session - Jupiter notebook on Factor Models20m
2 lecturas
References for module 2"Machine learning techniques for robust estimation of factor models"10m
Information on Jupyter notebook - Factor models10m
1 ejercicio de práctica
Module 2 Graded Quiz1h
Semana
3

Semana 3

2 horas para completar

Machine learning techniques for efficient portfolio diversification

2 horas para completar
7 videos (Total 59 minutos), 2 lecturas, 1 cuestionario
7 videos
Benefits of portfolio diversification8m
Portfolio diversification measures12m
Principle component analysis8m
Role of clustering6m
Graphical analysis8m
Selecting a portfolio of assets7m
2 lecturas
References for the module "Machine learning techniques for efficient portfolio diversification"10m
Reference for the module "Selecting a portfolio of assets"10m
1 ejercicio de práctica
Module 3 Graded Quiz45m
Semana
4

Semana 4

3 horas para completar

Machine learning techniques for regime analysis

3 horas para completar
7 videos (Total 65 minutos), 4 lecturas, 1 cuestionario
7 videos
Portfolio Decisions with Time-Varying Market Conditions10m
Trend filtering6m
A scenario based portfolio model8m
A two regime portfolio example7m
A multi regime model for a University Endowment9m
Lab session- Jupyter notebook on regime-based investment model15m
4 lecturas
Information on the "trend filtering" video2m
Information on "scenario based portfolio model" video2m
References for the module "Machine learning techniques for regime analysis"10m
Information on Jupyter notebookon regime-based investment model10m
1 ejercicio de práctica
Module 4 Graded Quiz1h

Acerca de Programa especializado Investment Management with Python and Machine Learning

The Data Science and Machine Learning for Asset Management Specialization has been designed to deliver a broad and comprehensive introduction to modern methods in Investment Management, with a particular emphasis on the use of data science and machine learning techniques to improve investment decisions.By the end of this specialization, you will have acquired the tools required for making sound investment decisions, with an emphasis not only on the foundational theory and underlying concepts, but also on practical applications and implementation. Instead of merely explaining the science, we help you build on that foundation in a practical manner, with an emphasis on the hands-on implementation of those ideas in the Python programming language through a series of dedicated lab sessions....
Investment Management with Python and Machine Learning

Preguntas Frecuentes

  • Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.

  • Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.

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