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Anastasia Diakaki
This course is uniquely tailored to the needs of investment professionals or those with investment industry knowledge who want to develop a basic, practical understanding of machine learning techniques and how they are used in the investment process. Incorporating real-life case studies, this course covers both the technical and the “soft skills” necessary for investment professionals to stay relevant.
In this course, you will learn how to:
- Distinguish between supervised and unsupervised machine learning and deep learning
- Describe how machine learning algorithm performance is evaluated
- Describe supervised and unsupervised...
Read more
This course is uniquely tailored to the needs of investment professionals or those with investment industry knowledge who want to develop a basic, practical understanding of machine learning techniques and how they are used in the investment process. Incorporating real-life case studies, this course covers both the technical and the “soft skills” necessary for investment professionals to stay relevant.
In this course, you will learn how to:
- Distinguish between supervised and unsupervised machine learning and deep learning
- Describe how machine learning algorithm performance is evaluated
- Describe supervised and unsupervised...
Read more
This course is uniquely tailored to the needs of investment professionals or those with investment industry knowledge who want to develop a basic, practical understanding of machine learning techniques and how they are used in the investment process. Incorporating real-life case studies, this course covers both the technical and the “soft skills” necessary for investment professionals to stay relevant.
In this course, you will learn how to:
- Distinguish between supervised and unsupervised machine learning and deep learning
- Describe how machine learning algorithm performance is evaluated
- Describe supervised and unsupervised machine learning algorithms and determine the problems they are best suited for
- Describe neural networks, deep learning nets, and reinforcement learning
- Choose an appropriate machine learning algorithm
- Describe the value of integrating machine learning and data projects in the investment process
- Work with data scientists and investment teams to harness information and insights from within large and alternative data sets
- Apply the CFA Institute Ethical Decision-Making Framework to machine learning dilemmas
This course is part of the Data Science for Investment Professionals Specialization offered by CFA Institute.
Register for this course and see more details by visiting:
OpenCourser.com/course/yk4uvd/machine
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Core audience consists of experienced financial professionals intending to expand their skills. Class objectives are specifically tailored for learners needing to utilize machine learning in investment
Analyzes the practical uses of machine learning for investment management using industry-relevant case studies
Builds both technical knowledge and soft skills necessary to navigate the increasingly tech-driven finance sector
Provides an ethical decision-making framework to help learners handle ethical challenges faced when employing machine learning in investments
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Save Machine Learning for Investment Professionals to your list so you can find it easily later:
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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 Machine Learning for Investment Professionals with these
activities:
Review of basic machine learning and deep learning concepts
Show steps
Brings students up to speed on fundamental concepts and creates a strong foundation for succeeding in this course
Show steps
-
Review key concepts of machine learning, such as supervised and unsupervised learning, and reinforcement learning
-
Review different types of deep learning neural networks, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers
-
Practice implementing basic machine learning algorithms using a programming language such as Python
Peer study group on machine learning and data science
Show steps
Provides a collaborative learning environment to discuss concepts, share knowledge, and work on projects together
Show steps
-
Join a study group with other students taking the course
-
Regularly meet to discuss course material, ask questions, and work on assignments together
-
Share resources, such as notes, articles, and code snippets
-
Collaborate on projects and assignments
Guided tutorials on working with large and alternative data sets
Show steps
Provides a practical understanding of working with real-world data, a key skill for investment professionals
Show steps
-
Follow tutorials on techniques for cleaning, preprocessing, and transforming large and complex datasets
-
Experiment with different data visualization techniques to explore and understand the data
-
Apply machine learning algorithms to identify patterns and insights from the data
Three other activities
Expand to see all activities and additional details
Show all six activities
Create a data visualization dashboard to track machine learning model performance
Show steps
Develops skills in data visualization and provides a valuable tool for monitoring and evaluating machine learning models
Browse courses on
Data Visualization
Show steps
-
Identify the key metrics to track
-
Choose an appropriate data visualization tool
-
Design and implement the dashboard
-
Regularly update the dashboard with the latest data and insights
Start a project on applying machine learning to a real-world investment problem
Show steps
Provides an opportunity to apply course concepts to a practical problem, develop problem-solving skills, and build a valuable portfolio piece
Browse courses on
Machine Learning Projects
Show steps
-
Identify an investment problem that can be addressed using machine learning
-
Gather and prepare the necessary data
-
Develop and implement a machine learning solution
-
Evaluate the performance of the solution and iterate as needed
-
Document the project and share the results
Create a machine learning project portfolio
Show steps
Provides a tangible demonstration of skills and knowledge, and a valuable asset for career advancement
Browse courses on
Machine Learning Projects
Show steps
-
Identify a real-world problem or challenge that can be solved using machine learning
-
Collect and prepare the necessary data
-
Develop and implement a machine learning solution
-
Evaluate the performance of the solution and iterate as needed
-
Document the project and present the results
Review of basic machine learning and deep learning concepts
Show steps
Brings students up to speed on fundamental concepts and creates a strong foundation for succeeding in this course
Show steps
Show steps
Show steps
- Review key concepts of machine learning, such as supervised and unsupervised learning, and reinforcement learning
- Review different types of deep learning neural networks, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers
- Practice implementing basic machine learning algorithms using a programming language such as Python
Peer study group on machine learning and data science
Show steps
Provides a collaborative learning environment to discuss concepts, share knowledge, and work on projects together
Show steps
Show steps
Show steps
- Join a study group with other students taking the course
- Regularly meet to discuss course material, ask questions, and work on assignments together
- Share resources, such as notes, articles, and code snippets
- Collaborate on projects and assignments
Guided tutorials on working with large and alternative data sets
Show steps
Provides a practical understanding of working with real-world data, a key skill for investment professionals
Show steps
Show steps
Show steps
- Follow tutorials on techniques for cleaning, preprocessing, and transforming large and complex datasets
- Experiment with different data visualization techniques to explore and understand the data
- Apply machine learning algorithms to identify patterns and insights from the data
Three other activities
Expand to see all activities and additional details
Show all six activities
Create a data visualization dashboard to track machine learning model performance
Show steps
Develops skills in data visualization and provides a valuable tool for monitoring and evaluating machine learning models
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Identify the key metrics to track
- Choose an appropriate data visualization tool
- Design and implement the dashboard
- Regularly update the dashboard with the latest data and insights
Start a project on applying machine learning to a real-world investment problem
Show steps
Provides an opportunity to apply course concepts to a practical problem, develop problem-solving skills, and build a valuable portfolio piece
Browse courses on
Machine Learning Projects
Show steps
Show steps
Show steps
- Identify an investment problem that can be addressed using machine learning
- Gather and prepare the necessary data
- Develop and implement a machine learning solution
- Evaluate the performance of the solution and iterate as needed
- Document the project and share the results
Create a machine learning project portfolio
Show steps
Provides a tangible demonstration of skills and knowledge, and a valuable asset for career advancement
Browse courses on
Machine Learning Projects
Show steps
Show steps
Show steps
- Identify a real-world problem or challenge that can be solved using machine learning
- Collect and prepare the necessary data
- Develop and implement a machine learning solution
- Evaluate the performance of the solution and iterate as needed
- Document the project and present the results
Career center
Learners who complete Machine Learning for Investment Professionals will develop knowledge and skills
that may be useful to these careers:
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts develop and implement mathematical models to analyze financial data and make investment decisions. This course can help aspiring Quantitative Analysts build a foundation in machine learning techniques, which are increasingly used in the field. The course covers supervised and unsupervised machine learning algorithms, as well as deep learning and reinforcement learning, providing a comprehensive overview of the latest advancements in the field.
Data Scientist
Data Scientist
Data Scientists use machine learning and other statistical techniques to extract insights from data. This course can help aspiring Data Scientists develop the skills they need to work with investment data, including how to choose the appropriate machine learning algorithm for a given problem, and how to evaluate algorithm performance. The course also covers the ethical implications of using machine learning, which is an important consideration for Data Scientists working in the investment industry.
Investment Analyst
Investment Analyst
Investment Analysts analyze financial data to make investment recommendations. This course can help aspiring Investment Analysts develop the skills they need to incorporate machine learning into their investment process. The course covers how to use machine learning to identify investment opportunities, and how to evaluate the performance of machine learning models. The course also covers the ethical implications of using machine learning in investment decision-making.
Portfolio Manager
Portfolio Manager
Portfolio Managers make investment decisions for their clients. This course can help aspiring Portfolio Managers develop the skills they need to incorporate machine learning into their investment process. The course covers how to use machine learning to identify investment opportunities, and how to evaluate the performance of machine learning models. The course also covers the ethical implications of using machine learning in investment decision-making.
Risk Manager
Risk Manager
Risk Managers assess and manage financial risks. This course can help aspiring Risk Managers develop the skills they need to use machine learning to identify and manage risks. The course covers how to use machine learning to identify potential risks, and how to evaluate the effectiveness of risk management strategies. The course also covers the ethical implications of using machine learning in risk management.
Financial Advisor
Financial Advisor
Financial Advisors provide financial advice to individuals and families. This course can help aspiring Financial Advisors develop the skills they need to incorporate machine learning into their financial planning process. The course covers how to use machine learning to identify investment opportunities, and how to evaluate the performance of machine learning models. The course also covers the ethical implications of using machine learning in financial planning.
Hedge Fund Manager
Hedge Fund Manager
Hedge Fund Managers manage hedge funds, which are investment funds that use advanced investment strategies. This course can help aspiring Hedge Fund Managers develop the skills they need to incorporate machine learning into their investment process. The course covers how to use machine learning to identify investment opportunities, and how to evaluate the performance of machine learning models. The course also covers the ethical implications of using machine learning in investment decision-making.
Private Equity Investor
Private Equity Investor
Private Equity Investors invest in private companies. This course can help aspiring Private Equity Investors develop the skills they need to use machine learning to identify investment opportunities. The course covers how to use machine learning to identify potential investment opportunities, and how to evaluate the potential of private companies.
Venture Capitalist
Venture Capitalist
Venture Capitalists invest in early-stage companies. This course can help aspiring Venture Capitalists develop the skills they need to use machine learning to identify investment opportunities. The course covers how to use machine learning to identify potential investment opportunities, and how to evaluate the potential of early-stage companies.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess financial risks. This course can help aspiring Actuaries develop the skills they need to use machine learning to assess financial risks. The course covers how to use machine learning to identify potential risks, and how to evaluate the effectiveness of risk management strategies.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data to make investment recommendations. This course may be useful for aspiring Financial Analysts who want to learn more about how machine learning is used in the investment industry. The course covers how to use machine learning to identify investment opportunities, and how to evaluate the performance of machine learning models.
Statistician
Statistician
Statisticians use mathematical and statistical techniques to analyze data. This course may be useful for aspiring Statisticians who want to learn more about how machine learning is used in the investment industry. The course covers how to use machine learning to identify investment opportunities, and how to evaluate the performance of machine learning models.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines. This course may be useful for aspiring Data Engineers who want to learn more about how machine learning is used in the investment industry. The course covers how to use machine learning to identify investment opportunities, and how to evaluate the performance of machine learning models.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, and maintain machine learning models. This course may be useful for aspiring Machine Learning Engineers who want to learn more about how machine learning is used in the investment industry. The course covers how to use machine learning to identify investment opportunities, and how to evaluate the performance of machine learning models.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course may be useful for aspiring Software Engineers who want to learn more about how machine learning is used in the investment industry. The course covers how to use machine learning to identify investment opportunities, and how to evaluate the performance of machine learning models.
For more career information including salaries, visit:
OpenCourser.com/course/yk4uvd/machine
Reading list
We've selected 11 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
Machine Learning for Investment Professionals.
Provides a comprehensive overview of machine learning techniques and their applications in asset management. It covers a wide range of topics, from supervised and unsupervised learning to deep learning and reinforcement learning. The book also includes case studies and examples from the financial industry.
Classic textbook on statistical learning. It covers a wide range of topics, from supervised and unsupervised learning to model selection and evaluation. The book valuable resource for anyone who wants to learn about the fundamentals of machine learning.
Practical guide to machine learning with Python. It covers a wide range of topics, from data collection and preparation to model selection and evaluation. The book also includes case studies and examples from the financial industry.
Save
Provides a practical guide to machine learning for business. It covers a wide range of topics, from the basics of machine learning to advanced techniques such as deep learning and reinforcement learning. The book also includes case studies and examples from the business world.
Save
Gentle introduction to machine learning. It covers a wide range of topics, from the basics of machine learning to advanced techniques such as deep learning and reinforcement learning. The book valuable resource for anyone who wants to learn about the basics of machine learning.
Provides a practical guide to machine learning with R. It covers a wide range of topics, from the basics of machine learning to advanced techniques such as deep learning and reinforcement learning. The book also includes case studies and examples from the financial industry.
Gentle introduction to machine learning for beginners. It covers a wide range of topics, from the basics of machine learning to advanced techniques such as deep learning and reinforcement learning. The book valuable resource for anyone who wants to learn about the basics of machine learning.
Practical guide to deep learning with Python. It covers a wide range of topics, from the basics of deep learning to advanced techniques such as convolutional neural networks and recurrent neural networks. The book valuable resource for anyone who wants to learn about the practical aspects of deep learning.
Comprehensive textbook on machine learning from a probabilistic perspective. It covers a wide range of topics, from the basics of probability to advanced techniques such as Bayesian inference and Markov chain Monte Carlo. The book valuable resource for anyone who wants to learn about the theoretical foundations of machine learning.
Classic textbook on reinforcement learning. It covers a wide range of topics, from the basics of reinforcement learning to advanced techniques such as deep reinforcement learning. The book valuable resource for anyone who wants to learn about the theoretical foundations of reinforcement learning.
Gentle introduction to Bayesian statistics with a focus on practical applications. It covers a wide range of topics, from the basics of probability to advanced techniques such as hierarchical Bayesian models and Markov chain Monte Carlo. The book valuable resource for anyone who wants to learn about the practical aspects of Bayesian statistics.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/yk4uvd/machine
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Time
25 hours
Via
Coursera
Institution
CFA Institute
Instructor
Anastasia Diakaki
This course belongs to a
collection
called
Data Science for Investment Professionals. It's comprised of three courses:
- Machine Learning for Investment Professionals (viewing)
- Statistics for Machine Learning for Investment Professionals
- Data and Statistics Foundation for Investment Professionals
Good to know
Core audience consists of experienced financial professionals intending to expand their skills. Class objectives are specifically tailored for learners needing to utilize machine learning in investment
Analyzes the practical uses of machine learning for investment management using industry-relevant case studies
Builds both technical knowledge and soft skills necessary to navigate the increasingly tech-driven finance sector
Provides an ethical decision-making framework to help learners handle ethical challenges faced when employing machine learning in investments
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