About this Course

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Intermediate Level
Approx. 15 hours to complete
English

What you will learn

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

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

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

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

Skills you will gain

Programming skillsManaging your own personal invetsmentsInvestment management knowledgeComputer ScienceExpertise in data science
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 15 hours to complete
English

Offered by

Placeholder

EDHEC Business School

Syllabus - What you will learn from this course

Week
1

Week 1

2 hours to complete

Introducing the fundamentals of machine learning

2 hours to complete
8 videos (Total 59 min), 4 readings, 1 quiz
8 videos
Introduction to machine-learning7m
Financial applications7m
Supervised learning7m
First algorithms7m
Highlights of best practice6m
Unsupervised learning7m
Challenges ahead10m
4 readings
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 practice exercise
Module 1Graded Quiz30m
Week
2

Week 2

4 hours to complete

Machine learning techniques for robust estimation of factor models

4 hours to complete
8 videos (Total 80 min), 2 readings, 1 quiz
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 readings
References for module 2"Machine learning techniques for robust estimation of factor models"10m
Information on Jupyter notebook - Factor models10m
1 practice exercise
Module 2 Graded Quiz1h
Week
3

Week 3

3 hours to complete

Machine learning techniques for efficient portfolio diversification

3 hours to complete
8 videos (Total 88 min), 2 readings, 1 quiz
8 videos
Benefits of portfolio diversification8m
Portfolio diversification measures12m
Principle component analysis8m
Role of clustering6m
Graphical analysis8m
Selecting a portfolio of assets7m
Graphical Network Analysis28m
2 readings
References for the module "Machine learning techniques for efficient portfolio diversification"10m
Reference for the module "Selecting a portfolio of assets"10m
1 practice exercise
Module 3 Graded Quiz45m
Week
4

Week 4

3 hours to complete

Machine learning techniques for regime analysis

3 hours to complete
7 videos (Total 65 min), 4 readings, 1 quiz
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 readings
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 practice exercise
Module 4 Graded Quiz1h

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About the Investment Management with Python and Machine Learning Specialization

Investment Management with Python and Machine Learning

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