Machine Learning for Investment Professionals from Coursera

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.

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Know what's good

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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

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

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Brings students up to speed on fundamental concepts and creates a strong foundation for succeeding in this course

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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

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Provides a collaborative learning environment to discuss concepts, share knowledge, and work on projects together

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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

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Provides a practical understanding of working with real-world data, a key skill for investment professionals

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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

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Create a data visualization dashboard to track machine learning model performance

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Develops skills in data visualization and provides a valuable tool for monitoring and evaluating machine learning models

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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

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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

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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

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Provides a tangible demonstration of skills and knowledge, and a valuable asset for career advancement

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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 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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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