Free Course
Machine Learning for Trading
by
Offered at Georgia Tech as CS 7646
About this Course
This course introduces students to the real world challenges of implementing machine learning based trading strategies including the algorithmic steps from information gathering to market orders. The focus is on how to apply probabilistic machine learning approaches to trading decisions. We consider statistical approaches like linear regression, KNN and regression trees and how to apply them to actual stock trading situations.
Course Cost
Free
Timeline
Approx. 4 months
Skill Level
intermediate
Included in Product
Rich Learning Content
Interactive Quizzes
Taught by Industry Pros
Self-Paced Learning
Join the Path to Greatness
Free Course
Machine Learning for Trading
Complete real-world projects designed by industry experts, covering topics from asset management to trading signal generation. Master AI algorithms for trading, and build your career-ready portfolio.
Course Leads
Tucker Balch
Instructor
Arpan Chakraborty
Instructor
Prerequisites and Requirements
Students should have strong coding skills and some familiarity with equity markets. No finance or machine learning experience is assumed.
Note that this course serves students focusing on computer science, as well as students in other majors such as industrial systems engineering, management, or math who have different experiences. All types of students are welcome!
The ML topics might be "review" for CS students, while finance parts will be review for finance students. However, even if you have experience in these topics, you will find that we consider them in a different way than you might have seen before, in particular with an eye towards implementation for trading.
Programming will primarily be in Python. We will make heavy use of numerical computing libraries like NumPy and Pandas.
See the Technology Requirements for using Udacity.