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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.
Overview
Syllabus
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- Welcome To Machine Learning for Investment Professionals
- Machine Learning
- Supervised Learning
- Unsupervised Learning
- Deep Learning
- The Translator
- Final Project
- This is a hands-on opportunity to apply what you have learned in this course and get prepared for the role of a "translator" between the investment team and the data science team. The project is designed to guide you through a case study of a real-life scenario you may encounter in your role as an investment professional who is working with machine learning methodologies. All gradable items in this project are required for you to get a course certificate.
