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.
이 강좌에 대하여
최근 조회 38,301회
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 4개 강좌 중 3번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
중급 단계
완료하는 데 약 15시간 필요
영어
자막: 영어
배울 내용
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
귀하가 습득할 기술
Programming skillsManaging your own personal invetsmentsInvestment management knowledgeComputer ScienceExpertise in data science
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 4개 강좌 중 3번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
중급 단계
완료하는 데 약 15시간 필요
영어
자막: 영어
강의 계획 - 이 강좌에서 배울 내용
완료하는 데 2시간 필요
Introducing the fundamentals of machine learning
완료하는 데 2시간 필요
8개 동영상 (총 59분), 4 개의 읽기 자료, 1 개의 테스트
8개의 동영상
Introduction to machine-learning7m
Financial applications7m
Supervised learning7m
First algorithms7m
Highlights of best practice6m
Unsupervised learning7m
Challenges ahead10m
4개의 읽기 자료
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
1개 연습문제
Module 1Graded Quiz30m
완료하는 데 4시간 필요
Machine learning techniques for robust estimation of factor models
완료하는 데 4시간 필요
8개 동영상 (총 80분), 2 개의 읽기 자료, 1 개의 테스트
8개의 동영상
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
2개의 읽기 자료
References for module 2"Machine learning techniques for robust estimation of factor models"10m
Information on Jupyter notebook - Factor models10m
1개 연습문제
Module 2 Graded Quiz1시간
완료하는 데 2시간 필요
Machine learning techniques for efficient portfolio diversification
완료하는 데 2시간 필요
7개 동영상 (총 59분), 2 개의 읽기 자료, 1 개의 테스트
7개의 동영상
Benefits of portfolio diversification8m
Portfolio diversification measures12m
Principle component analysis8m
Role of clustering6m
Graphical analysis8m
Selecting a portfolio of assets7m
2개의 읽기 자료
References for the module "Machine learning techniques for efficient portfolio diversification"10m
Reference for the module "Selecting a portfolio of assets"10m
1개 연습문제
Module 3 Graded Quiz45m
완료하는 데 3시간 필요
Machine learning techniques for regime analysis
완료하는 데 3시간 필요
7개 동영상 (총 65분), 4 개의 읽기 자료, 1 개의 테스트
7개의 동영상
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
4개의 읽기 자료
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
1개 연습문제
Module 4 Graded Quiz1시간
검토
PYTHON AND MACHINE LEARNING FOR ASSET MANAGEMENT 의 최상위 리뷰
AT 제공2020년 3월 1일
would be good to focus more on the jupyter notebooks and less on multiple choice. Really interesting notebooks and quite advanced / technical material which deserves more time and coverage.
KA 제공2020년 2월 5일
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.
ST 제공2020년 4월 9일
The topics covered in this course are really interesting. I learned a great deal by studying various papers covered in this course - Thank you to both instructors!
AJ 제공2020년 5월 30일
Please consider adding additional videos for the lab sessions, as one can not gain the Machine Learning python coding skills from PPT slides!
Investment Management with Python and Machine Learning 특화 과정 정보
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.
자주 묻는 질문
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.
What is the refund policy?
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.
Is financial aid available?
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.
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