Python and Machine Learning for Asset Management
- Offered byCoursera
Python and Machine Learning for Asset Management at Coursera Overview
Duration | 16 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Python and Machine Learning for Asset Management at Coursera Highlights
- This Course Plus the Full Specialization.
- Shareable Certificates.
- Graded Programming Assignments.
Python and Machine Learning for Asset Management at Coursera Course details
- This course will enable you mastering machine-learning approaches in the area of investment management. It has been designed by two thought leaders in their field, Lionel Martellini from EDHEC-Risk Institute and John Mulvey from Princeton University. Starting from the basics, they will help you build practical skills to understand data science so you can make the best portfolio decisions.
- The course will start with an introduction to the fundamentals of machine learning, followed by an in-depth discussion of the application of these techniques to portfolio management decisions, including the design of more robust factor models, the construction of portfolios with improved diversification benefits, and the implementation of more efficient risk management models.
- We have designed a 3-step learning process: first, we will introduce a meaningful investment problem and see how this problem can be addressed using statistical techniques. Then, we will see how this new insight from Machine learning can complete and improve the relevance of the analysis.
- You will have the opportunity to capitalize on videos and recommended readings to level up your financial expertise, and to use the quizzes and Jupiter notebooks to ensure grasp of concept.
- At the end of this course, you will master the various machine learning techniques in investment management.
Python and Machine Learning for Asset Management at Coursera Curriculum
Introducing the fundamentals of machine learning
Welcome to the Python Machine-Learning for Investment Management course
Introduction to machine-learning
Financial applications
Supervised learning
First algorithms
Highlights of best practice
Unsupervised learning
Challenges ahead
Requirements
Material at your disposal
Machine Learning for Investment Decisions: A Brief Guided Tour
References for module 1"Introducing the fundamentals of machine learning"
Module 1Graded Quiz
Machine learning techniques for robust estimation of factor models
Introduction to module 2 - Basics of factor investing
Introducing Factor Models
Typology of factor models
Using factor models in portfolio construction and analysis
Penalty methods
Setting factor loadings and examples
Shrinkage concepts
Lab session - Jupiter notebook on Factor Models
References for module 2"Machine learning techniques for robust estimation of factor models"
Information on Jupyter notebook - Factor models
Module 2 Graded Quiz
Machine learning techniques for efficient portfolio diversification
Introduction to module 3 -Machine learning techniques for efficient portfolio diversification
Benefits of portfolio diversification
Portfolio diversification measures
Principle component analysis
Role of clustering
Graphical analysis
Selecting a portfolio of assets
Graphical Network Analysis
References for the module "Machine learning techniques for efficient portfolio diversification"
Reference for the module "Selecting a portfolio of assets"
Module 3 Graded Quiz
Machine learning techniques for regime analysis
Introduction to economic regimes
Portfolio Decisions with Time-Varying Market Conditions
Trend filtering
A scenario based portfolio model
A two regime portfolio example
A multi regime model for a University Endowment
Lab session- Jupyter notebook on regime-based investment model
Information on the "trend filtering" video
Information on "scenario based portfolio model" video
References for the module "Machine learning techniques for regime analysis"
Information on Jupyter notebookon regime-based investment model
Module 4 Graded Quiz
Identifying recessions, crash regimes and feature selection
Introduction to module 5
Traditional approaches
Machine-Learning Processes
Several Machine Learning Methods
Predicting recessions
Challenges ahead
Lab session - Jupiter notebook on Forecasting recessions with machine-learning
References for the module "Identifying recessions, crash regimes and features selection"
Information on Jupyter notebook on Forecasting recession with machine learning
To be continued (3)
Module 5 Graded Quiz
Other courses offered by Coursera
Student Forum
Anything you would want to ask experts?
Data ScienceMachine LearningMachine Learning and Pattern RecognitionPython and Machine Learning for Asset Management