Machine Learning for Investment Professionals (Coursera)

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

Course 3 of 3 in the Data Science for Investment Professionals Specialization.
What You Will Learn

• Describe how machine learning applications can address real-world investment problems
• Explain machine learning concepts and techniques to a non-expert audience
• Utilize the language of data science when working with data scientists and data engineers
• Apply the CFA Institute Ethical Decision-Making Framework to machine learning dilemmas

Syllabus

WEEK 1
Welcome To Machine Learning for Investment Professionals
Machine Learning

WEEK 2
Supervised Learning

WEEK 3
Unsupervised Learning

WEEK 4
Deep Learning

WEEK 5
The Translator

WEEK 6
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.