YoVDO

Machine Learning for Investment Professionals

Offered By: CFA Institute via Coursera

Tags

Investment Courses Machine Learning Courses Deep Learning Courses Supervised Learning Courses Unsupervised Learning Courses Reinforcement Learning Courses Neural Networks Courses Ethical Decision-Making Courses

Course Description

Overview

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.

Taught by

Anastasia Diakaki

Related Courses

Neural Networks for Machine Learning
University of Toronto via Coursera
機器學習技法 (Machine Learning Techniques)
National Taiwan University via Coursera
Machine Learning Capstone: An Intelligent Application with Deep Learning
University of Washington via Coursera
Прикладные задачи анализа данных
Moscow Institute of Physics and Technology via Coursera
Leading Ambitious Teaching and Learning
Microsoft via edX