About this Course

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Flexible deadlines

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Intermediate Level

Approx. 15 hours to complete

English

Subtitles: 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

Subtitles: English

Offered by

EDHEC Business School logo

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

2 hours to complete

Machine learning techniques for efficient portfolio diversification

2 hours to complete
7 videos (Total 59 min), 2 readings, 1 quiz
7 videos
Benefits of portfolio diversification8m
Portfolio diversification measures12m
Principle component analysis8m
Role of clustering6m
Graphical analysis8m
Selecting a portfolio of assets7m
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

About the Investment Management with Python and Machine Learning Specialization

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

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • 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.

  • 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.

  • 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.

  • This Course doesn't carry university credit, but some universities may choose to accept Course Certificates for credit. Check with your institution to learn more. Online Degrees and Mastertrack™ Certificates on Coursera provide the opportunity to earn university credit.

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