Financial markets are fickle beasts that can be extremely difficult to navigate for the average investor. This Quant Trading Using Machine Learning course will introduce you to machine learning, a field of study that gives computers the ability to learn without being explicitly programmed, while teaching you how to apply these techniques to quantitative trading. Using Python libraries, you’ll discover how to build sophisticated financial models that will better inform your investing decisions. Ideally, this one will buy itself back and then some!

What am I going to get from this course?

• Develop Quant Trading models using advanced Machine Learning techniques
• Compare and evaluate strategies using Sharpe Ratios
• Use techniques like Random Forests and K-Nearest Neighbors to develop Quant Trading models
• Use Gradient Boosted trees and tune them for high performance
• Use techniques like Feature engineering, parameter tuning and avoiding overfitting
• Build an end-to-end application from data collection and preparation to model selection

Course Requirements:

• Working knowledge of Python is necessary if you want to run the source code that is provided.
• Basic knowledge of machine learning, especially Machine Learning classification techniques, would be helpful but it’s not mandatory.

What is the target audience?

• Quant traders who have not used Machine learning techniques before to develop trading strategies
• Analytics professionals, modelers, big data professionals who want to get hands-on experience with Machine Learning
• Anyone who is interested in Machine Learning and wants to learn through a practical, project-based approach