This course is part of the Investment Management with Python and Machine Learning Specialization

3.1
stars
304 ratings

John Mulvey - Princeton University +1 more instructor

15,262 already enrolled

Offered By

Investment Management with Python and Machine Learning SpecializationEDHEC Business School

About this Course

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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.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

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Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 3 of 4 in the

Investment Management with Python and Machine Learning Specialization

Intermediate Level

Approx. 16 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 skills
Managing your own personal invetsments
Investment management knowledge
Computer Science
Expertise in data science

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 3 of 4 in the

Investment Management with Python and Machine Learning Specialization

Intermediate Level

Approx. 16 hours to complete

English

Instructors

Instructor rating

3.25/5 (70 Ratings)

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

15,262 Learners

1 Course

Lionel Martellini, PhD

EDHEC-Risk Institute, Director

Finance

61,171 Learners

3 Courses

Offered by

EDHEC Business School

Founded in 1906, EDHEC is now one of Europe’s top 15 business schools .

Based in Lille, Nice, Paris, London and Singapore, and counting over 90 nationalities on its campuses, EDHEC is a fully international school directly connected to the business world. With over 40,000 graduates in 120 countries, it trains committed managers capable of dealing with the challenges of a fast-evolving world. Harnessing its core values of excellence, innovation and entrepreneurial spirit, EDHEC has developed a strategic model founded on research of true practical use to society, businesses and students, and which is particularly evident in the work of EDHEC-Risk Institute and Scientific Beta. The School functions as a genuine laboratory of ideas and plays a pioneering role in the field of digital education via EDHEC Online, the first fully online degree-level training platform. These various components make EDHEC a centre of knowledge, experience and diversity, geared to preparing new generations of managers to excel in a world subject to transformational change. EDHEC in figures: 8,600 students in academic education, 19 degree programmes ranging from bachelor to PhD level, 184 professors and researchers, 11 specialist research centres.

Syllabus - What you will learn from this course

Week1

Week 1

4 hours to complete

Introducing the fundamentals of machine learning

4 hours to complete

9 videos (Total 102 min), 5 readings, 1 quiz

9 videos

Welcome to the Python Machine-Learning for Investment management course6mIntroduction to machine-learning7mFinancial applications7mSupervised learning7mFirst algorithms7mHighlights of best practice6mUnsupervised learning7mChallenges ahead10mLab session optimal portfolio40m

5 readings

Requirements2mMaterial at your disposal2mMachine Learning for Investment Decisions: A Brief Guided Tour10mReferences for module 1"Introducing the fundamentals of machine learning"10mLab session optimal portfolio10m

1 practice exercise

Module 1Graded Quiz30m

Week2

Week 2

3 hours to complete

Machine learning techniques for robust estimation of factor models

3 hours to complete

8 videos (Total 124 min), 1 reading, 1 quiz

8 videos

Introduction to module 2 - Basics of factor investing8mIntroducing Factor Models7mTypology of factor models9mUsing factor models in portfolio construction and analysis10mPenalty methods9mSetting factor loadings and examples7mShrinkage concepts7mLab session - Jupiter notebook on Factor Models1h 3m

1 reading

References for module 2"Machine learning techniques for robust estimation of factor models"10m

1 practice exercise

Module 2 Graded Quiz1h

Week3

Week 3

3 hours to complete

Machine learning techniques for efficient portfolio diversification

3 hours to complete

8 videos (Total 88 min), 3 readings, 1 quiz

8 videos

Introduction to module 3 -Machine learning techniques for efficient portfolio diversification7mBenefits of portfolio diversification8mPortfolio diversification measures12mPrinciple component analysis8mRole of clustering6mGraphical analysis8mSelecting a portfolio of assets7mLab session: Graphical Network Analysis28m

3 readings

Supplementary material PCA10mReferences for the module "Machine learning techniques for efficient portfolio diversification"10mReference for the module "Selecting a portfolio of assets"10m

1 practice exercise

Module 3 Graded Quiz45m

Week4

Week 4

3 hours to complete

Machine learning techniques for regime analysis

3 hours to complete

7 videos (Total 112 min), 4 readings, 1 quiz

7 videos

Introduction to economic regimes7mPortfolio Decisions with Time-Varying Market Conditions10mTrend filtering6mA scenario based portfolio model8mA two regime portfolio example7mA multi regime model for a University Endowment9mNEW Lab session- Jupyter notebook on regime-based investment model1h 2m

4 readings

Information on the "trend filtering" video2mInformation on "scenario based portfolio model" video2mReferences for the module "Machine learning techniques for regime analysis"10mRegime-aware asset allocation10m

1 practice exercise

Module 4 Graded Quiz1h

Reviews

3.1

127 reviews

5 stars
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TOP REVIEWS FROM PYTHON AND MACHINE LEARNING FOR ASSET MANAGEMENT

by AJMay 30, 2020

Please consider adding additional videos for the lab sessions, as one can not gain the Machine Learning python coding skills from PPT slides!

by LTFeb 17, 2021

Good overview on Machine Learning techniques, need for some basic knowledge in statistics and Python for an optimized experience.

by FNAug 20, 2022

The ideas did not explain clearly and explicitly. They want to cover many topics but all in general so that you do not understand deeply what is going on.

by KAFeb 5, 2020

Good concepts to touch but lack on coding in granulality example. But overall, I'm get a good example how to implement machine learning technique to finance perspective.

View all reviews

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.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
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.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

Python and Machine Learning for Asset Management

About this Course

What you will learn

Skills you will gain