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John Mulvey - Princeton University
and
Claudia Carrone
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.
Read more
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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/0prnl3/python
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What's inside
Syllabus
Introducing the fundamentals of machine learning
Machine learning techniques for robust estimation of factor models
Machine learning techniques for efficient portfolio diversification
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Syllabus
Introducing the fundamentals of machine learning
Machine learning techniques for robust estimation of factor models
Machine learning techniques for efficient portfolio diversification
Read more
Machine learning techniques for regime analysis
Identifying recessions, crash regimes and feature selection
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches techniques for improved portfolio diversification
Taught by thought leaders in investment management
Develops foundational machine learning skills
Applies techniques to real-world investment scenarios
Requires students to have some background in finance
Covers a niche topic that may not be relevant to all learners
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Reviews summary
Princeton python and machine learning for asset management
Learners say this course offers an interesting overview of machine learning applications to asset management, with particularly strong Jupyter Notebooks. However, it has many negative reviews due to poor quality video lectures, confusing quizzes, and a lack of practical, hands on application. This course is not well-suited for beginners and requires a foundational understanding of Python and statistics.
This course offers Jupyter Notebooks that are well-liked by students. The lab sessions however, often receive negative reviews, with complaints about being poorly produced, lacking in explanations, and not corresponding with the topics covered in the lectures and quizzes.
"Super interesting, very well explained, with lots of useful resources (links to various papers and textbooks), and, best of all, with very practical, well-annotated notebooks applying the theory covered in the video lessons."
"The lab session is not well instructed."
"The lab sessions could be way better."
"The labs contain good material but are poorly packaged"
This course has a mixed review on its materials with many negative comments on the lectures and quizzes but many positive comments on the Jupyter Notebooks provided.
"Very nice course sharing many types of knowledges around data / cleaning / type of data / several algorithms / organised Python coding"
"The Jupyter sessions are well-annotated applying the theory covered in the video lessons."
"video lectures, confusing quizzes, and a lack of practical, hands on application."
The video lectures in this course are often criticized for being confusing, poorly explained, poorly produced, and lacking in real-world examples and applications.
"Significant drop in teaching quality compared to the first two courses of the specialization."
"The lecturers read what is written on the slides and that's it."
"The video sessions are painful to watch, the content is incoherent, the quizzes are poorly constructed."
"The Princeton parts were interesting if I want to be kind but not really useful."
"This course left with a lot to be desired. First the repitions from MooC 1 & 2 were substantial. Course rushed through the Machine learning principles"
The quizzes in this course are also frequently criticized for being poorly written and often not related to the material covered in the lectures and labs.
"The quizzes were terrible."
"Quizzes are horrible."
"The quizzes are ambiguous, often non numerical and didn't rely enough on interaction with the notebooks"
This course is not well-suited for beginners and requires a foundational understanding of Python and statistics.
"This course is not for beginners as it quickly covers insights instead of details."
"The course assumes you have some knowledge of ML and some Python coding experience."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Python and Machine Learning for Asset Management with these
activities:
Review Python basics
Show steps
Review the basics of Python to ensure a solid foundation for the course.
Browse courses on
Python
Show steps
-
Go over basic data types and structures
-
Practice writing simple functions
-
Review object-oriented programming concepts
Mentor or tutor other students in the course
Show steps
Enhance understanding of the course material by helping others, solidifying knowledge and developing communication skills.
Browse courses on
Machine Learning
Show steps
-
Identify opportunities to assist other students
-
Review the course material and identify areas where you can provide support
-
Offer help through online forums or study groups
Follow tutorials on machine learning algorithms
Show steps
Expand on the understanding of machine learning algorithms covered in the course by following guided tutorials.
Browse courses on
Machine Learning
Show steps
-
Identify relevant tutorials for specific algorithms
-
Work through the tutorials step-by-step
-
Implement the algorithms in Python
Four other activities
Expand to see all activities and additional details
Show all seven activities
Solve practice problems on machine learning
Show steps
Solidify the understanding of machine learning concepts by solving practice problems.
Browse courses on
Machine Learning
Show steps
-
Find practice problems from online resources or textbooks
-
Solve the problems using Python or other programming languages
-
Compare solutions with others to identify areas for improvement
Create a collection of resources on machine learning
Show steps
Organize and expand knowledge by compiling a collection of valuable resources related to machine learning.
Browse courses on
Machine Learning
Show steps
-
Identify and gather relevant resources, such as articles, tutorials, and datasets
-
Organize the resources into a structured collection
-
Provide annotations or summaries to aid understanding
Create a blog or presentation on machine learning applications
Show steps
Deepen the understanding of machine learning by creating a blog or presentation on its applications.
Browse courses on
Machine Learning
Show steps
-
Identify a specific industry or domain
-
Research and gather information on machine learning applications in that domain
-
Create a blog or presentation that explains the applications and their benefits
Build a machine learning model for a real-world problem
Show steps
Apply the knowledge gained in the course to a practical project, solidifying understanding and developing problem-solving skills.
Browse courses on
Machine Learning
Show steps
-
Identify a real-world problem that can be addressed with machine learning
-
Collect and prepare the necessary data
-
Develop and train a machine learning model
-
Evaluate the model's performance
-
Deploy the model and monitor its performance
Review Python basics
Show steps
Review the basics of Python to ensure a solid foundation for the course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Go over basic data types and structures
- Practice writing simple functions
- Review object-oriented programming concepts
Mentor or tutor other students in the course
Show steps
Enhance understanding of the course material by helping others, solidifying knowledge and developing communication skills.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify opportunities to assist other students
- Review the course material and identify areas where you can provide support
- Offer help through online forums or study groups
Follow tutorials on machine learning algorithms
Show steps
Expand on the understanding of machine learning algorithms covered in the course by following guided tutorials.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify relevant tutorials for specific algorithms
- Work through the tutorials step-by-step
- Implement the algorithms in Python
Four other activities
Expand to see all activities and additional details
Show all seven activities
Solve practice problems on machine learning
Show steps
Solidify the understanding of machine learning concepts by solving practice problems.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find practice problems from online resources or textbooks
- Solve the problems using Python or other programming languages
- Compare solutions with others to identify areas for improvement
Create a collection of resources on machine learning
Show steps
Organize and expand knowledge by compiling a collection of valuable resources related to machine learning.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify and gather relevant resources, such as articles, tutorials, and datasets
- Organize the resources into a structured collection
- Provide annotations or summaries to aid understanding
Create a blog or presentation on machine learning applications
Show steps
Deepen the understanding of machine learning by creating a blog or presentation on its applications.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify a specific industry or domain
- Research and gather information on machine learning applications in that domain
- Create a blog or presentation that explains the applications and their benefits
Build a machine learning model for a real-world problem
Show steps
Apply the knowledge gained in the course to a practical project, solidifying understanding and developing problem-solving skills.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify a real-world problem that can be addressed with machine learning
- Collect and prepare the necessary data
- Develop and train a machine learning model
- Evaluate the model's performance
- Deploy the model and monitor its performance
Career center
Learners who complete Python and Machine Learning for Asset Management will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist uses their mathematical and statistical skills to extract insights from data. They use these insights to solve problems, and develop new products and services. This course "Python and Machine Learning for Asset Management" will enhance a Data Scientist's understanding of Machine Learning. This course will also allow them to apply those Machine Learning techniques to the field of finance, making them more valuable to their employers.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer builds and maintains Machine Learning models. They work closely with Data Scientists to ensure that models are accurate and efficient. This course "Python and Machine Learning for Asset Management" will enhance a Machine Learning Engineer's understanding of Machine Learning, specifically its application to the field of finance. This course will also provide the tools and knowledge needed to build and maintain Machine Learning models that will improve financial decision making.
Financial Analyst
Financial Analyst
A Financial Analyst researches investments and provides advice to clients. They use Machine Learning techniques to estimate financial risk, and are constantly looking for ways to improve their clients' financial situations. This course "Python and Machine Learning for Asset Management" will allow a Financial Analyst to thoroughly understand Machine Learning. This will allow an analyst to make more informed decisions, and provide better advice to their clients.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical models to analyze financial data. They use Machine Learning to develop trading strategies with improved diversification and efficiency. This course "Python and Machine Learning for Asset Management" will enhance understanding of Machine Learning, allowing a Quantitative Analyst to identify recessions, crash regimes, and feature selection, which can be used to their advantage when developing trading strategies.
Portfolio Manager
Portfolio Manager
A Portfolio Manager makes investment decisions for a group of clients. They oversee the day-to-day operations of the portfolio, and make changes as needed. This course "Python and Machine Learning for Asset Management" will provide the tools and knowledge needed by a Portfolio Manager to make the best portfolio decisions. The course's in-depth discussion of Machine Learning will allow a Portfolio Manager to build practical skills to understand data science, and apply those skills to their work.
Risk Manager
Risk Manager
A Risk Manager identifies and assesses financial risks, and develops strategies to mitigate those risks. They are constantly looking for ways to improve the resilience of their organization. This course "Python and Machine Learning for Asset Management" will allow a Risk Manager to gain a deep understanding of Machine Learning. This understanding will allow them to develop more effective risk management models and protect their organization from financial losses.
Investment Analyst
Investment Analyst
An Investment Analyst researches, analyzes, and provides recommendations on investment opportunities. These recommendations include making buy, sell, or hold decisions. This course "Python and Machine Learning for Asset Management" will establish a foundation in the Machine Learning techniques used by Investment Analysts on a daily basis. These techniques improve financial expertise, and allow an analyst to capitalize on videos and recommended readings to further enhance their career.
Financial Planner
Financial Planner
A Financial Planner helps clients achieve their financial goals. They create and manage financial plans, and provide advice on investments, retirement, and other financial matters. This course "Python and Machine Learning for Asset Management" will provide a Financial Planner with a better understanding of Machine Learning, and how these techniques can be applied to financial planning. This course may also enhance their ability to develop custom financial plans for their clients.
Economist
Economist
An Economist studies the economy and how it affects businesses and individuals. They use their knowledge to make predictions about the future, and to develop policies that will improve the economy. This course "Python and Machine Learning for Asset Management" will teach an Economist about Machine Learning, and its applications in the field of finance. This course may be especially helpful for Economists who are interested in working in the financial industry.
Actuary
Actuary
An Actuary uses mathematical and statistical skills to assess financial risks. They work in a variety of industries, including insurance, finance, and healthcare. This course "Python and Machine Learning for Asset Management" will provide an Actuary with a strong foundation in Machine Learning, and how it relates to financial risk. This course will also allow them to apply those Machine Learning techniques to their work, making them more valuable to their employers.
Statistician
Statistician
A Statistician collects, analyzes, and interprets data. They use their skills to solve problems, and to make informed decisions. This course "Python and Machine Learning for Asset Management" will provide a Statistician with a deep understanding of Machine Learning, and how it can be applied to the field of finance. This course may also enhance their ability to solve complex statistical problems.
Software Engineer
Software Engineer
A Software Engineer designs, develops, tests, and maintains software systems. They work on a variety of projects, from small personal apps to large enterprise systems. This course "Python and Machine Learning for Asset Management" will teach a Software Engineer about Machine Learning, and how those techniques are applied in financial settings. This knowledge will allow them to develop more effective and efficient software systems for financial institutions.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical and statistical models to improve the efficiency of organizations. They work on a variety of projects, from optimizing supply chains to scheduling staff. This course "Python and Machine Learning for Asset Management" will provide an Operations Research Analyst with a strong foundation in Machine Learning, and how it can be applied to their work. This course will also allow them to develop more effective and efficient models, making them more valuable to their employers.
Business Analyst
Business Analyst
A Business Analyst uses their skills in data analysis, process improvement, and problem solving to help businesses make better decisions. This course "Python and Machine Learning for Asset Management" will teach a Business Analyst about Machine Learning, and its applications in the field of finance. This course may also enhance their ability to analyze data and make sound business decisions.
Consultant
Consultant
A Consultant provides expert advice to organizations on a variety of topics. They work on a variety of projects, from developing marketing strategies to improving financial performance. This course "Python and Machine Learning for Asset Management" will provide a Consultant with a strong foundation in Machine Learning, and how it can be applied to the field of finance. This course will also allow them to provide more valuable advice to their clients, making them more successful in their careers.
For more career information including salaries, visit:
OpenCourser.com/course/0prnl3/python
Reading list
We've selected nine books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Python and Machine Learning for Asset Management .
Is exclusively focused on applying machine learning techniques to asset management. It covers background prerequisite knowledge in both finance and machine learning. It valuable reference containing many code examples in Python and R
Provides a solid background in machine learning with Python and scikit-learn. It covers fundamental concepts, algorithms, and applications of machine learning. It is helpful as a general-purpose reference on machine learning.
Save
Comprehensive guide to deep learning. It covers the fundamental concepts, algorithms, and applications of deep learning. It valuable reference for anyone who wants to learn more about deep learning.
Provides a comprehensive introduction to Python for data analysis. It covers the basics of Python, data manipulation, data visualization, and machine learning. It valuable reference for anyone who wants to use Python for data analysis.
Provides a comprehensive introduction to machine learning with R. It covers the basics of machine learning, data manipulation, data visualization, and machine learning algorithms. It valuable reference for anyone who wants to use R for machine learning.
Provides a practical guide to machine learning with Python and scikit-learn, Keras, and TensorFlow. It covers the basics of machine learning, data manipulation, data visualization, and machine learning algorithms. It valuable reference for anyone who wants to use Python for machine learning.
Provides a comprehensive introduction to machine learning for algorithmic trading. It covers the basics of machine learning, data manipulation, data visualization, and machine learning algorithms. It valuable reference for anyone who wants to use machine learning for algorithmic trading.
Provides a comprehensive introduction to machine learning for portfolio optimization. It covers the basics of machine learning, data manipulation, data visualization, and machine learning algorithms. It valuable reference for anyone who wants to use machine learning for portfolio optimization.
Provides a comprehensive introduction to machine learning for financial forecasting. It covers the basics of machine learning, data manipulation, data visualization, and machine learning algorithms. It valuable reference for anyone who wants to use machine learning for financial forecasting.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/0prnl3/python
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Effort
5 weeks - 2/3 hours per week
Level
Intermediate
Via
Coursera
Institution
EDHEC Business School
Instructors
John Mulvey - Princeton University
Claudia Carrone
Language
English
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This course belongs to a
collection
called
Investment Management with Python and Machine Learning. It's comprised of four courses:
- Advanced Portfolio Construction and Analysis with Python
- Introduction to Portfolio Construction and Analysis with Python
- Python and Machine Learning for Asset Management (viewing)
- Python and Machine-Learning for Asset Management with Alternative Data Sets
Good to know
Teaches techniques for improved portfolio diversification
Taught by thought leaders in investment management
Develops foundational machine learning skills
Applies techniques to real-world investment scenarios
Requires students to have some background in finance
Covers a niche topic that may not be relevant to all learners
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