Georgia Institute of Technology - Machine Learning for Trading
5.0 /5- Offered byUDACITY
Machine Learning for Trading at UDACITY Overview
Machine Learning for Trading
at UDACITY
Offered at Georgia Tech as CS 7646
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Machine Learning for Trading at UDACITY Highlights
Machine Learning for Trading
at UDACITY
- Highly-curated content
Taught by industry experts
Self-paced progress
Machine Learning for Trading at UDACITY Course details
Machine Learning for Trading
at UDACITY
Skills you will learn
More 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
Machine Learning for Trading at UDACITY Curriculum
Machine Learning for Trading
at UDACITY
Machine Learning for Trading at UDACITY Faculty details
Machine Learning for Trading
at UDACITY
Arpan Chakraborty
Designation : Instructor
Tucker Balch
Designation : Instructor
Machine Learning for Trading at UDACITY Entry Requirements
Machine Learning for Trading
at UDACITY
Eligibility criteria
- 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.
Conditional Offer
- Not mentioned
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