Python and Machine Learning for Asset Management
- 3
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Approx. 17 hours to complete
Course Summary
This course teaches how to use Python for machine learning in investment management. Students will learn how to build and evaluate investment strategies using techniques such as linear regression, decision trees, and random forests.Key Learning Points
- Learn how to use Python for machine learning in investment management
- Build and evaluate investment strategies using linear regression, decision trees, and random forests
- Gain practical experience through hands-on coding assignments and a final project
Job Positions & Salaries of people who have taken this course might have
- Quantitative Analyst
- USA: $80,000 - $170,000
- India: INR 7,00,000 - INR 18,00,000
- Spain: €30,000 - €80,000
- Investment Analyst
- USA: $60,000 - $120,000
- India: INR 5,00,000 - INR 12,00,000
- Spain: €25,000 - €60,000
- Data Scientist
- USA: $100,000 - $180,000
- India: INR 8,00,000 - INR 20,00,000
- Spain: €40,000 - €90,000
Related Topics for further study
Learning Outcomes
- Ability to build and evaluate investment strategies using machine learning techniques
- Practical experience with Python coding and data analysis
- Understanding of the role of machine learning in investment management
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of Python programming
- Familiarity with investment management concepts
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Machine Learning for Trading
- Algorithmic Trading and Quantitative Analysis
Related Education Paths
Notable People in This Field
- Yves Hilpisch
- Wes McKinney
Related Books
Description
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.
Knowledge
- 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
Outline
- 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
- Lab session optimal portfolio
- Requirements
- Material at your disposal
- Machine Learning for Investment Decisions: A Brief Guided Tour
- References for module 1"Introducing the fundamentals of machine learning"
- Lab session optimal portfolio
- 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
- Lab session: Graphical Network Analysis
- Supplementary material PCA
- References for the module "Machine learning techniques for efficient portfolio diversification"
- Reference for the module "Selecting a portfolio of assets"
- Lab session: Graphical Network Analysis
- 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 5: Regime Prediction 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
Summary of User Reviews
Read reviews of Coursera's Python Machine Learning for Investment Management course. Users rate this course highly for its practical approach to teaching machine learning for finance. Many users appreciate the instructor's clear explanations and the real-world examples used throughout the course.Key Aspect Users Liked About This Course
The practical approach to teaching machine learning for finance is highly appreciated by many users.Pros from User Reviews
- Clear explanations by the instructor
- Real-world examples used throughout the course
- Good balance between theory and practice
- Course content is well-organized
- Helpful peer assessments
Cons from User Reviews
- Some users found the course to be too basic
- Lack of interaction with the instructor
- Some users thought the course was too short
- Some users found the quizzes to be too difficult
- Not enough emphasis on coding
John Mulvey - Princeton University
3 Raiting
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