Rs. 1999 Rs. 599
Complete guide for Quants Trading using Machine Learning
Created by Stanford and IIT alumni with work experience in Google and Microsoft, this course teaches techniques of Machine Learning that can be combined with Quantitative Trading to design powerful models.
Quantitative Trading consists of trading strategies that are formed after quantitatively analysing data, while Machine Learning is a data analysis methodology that automates analytical model building. This course explains how the approaches of Machine Learning can be combined with Quantitative Trading to design powerful Quant Trading models.
This course on Quants Trading in Machine Learning introduces you to the basics and the traditional ways to develop Quants Trading, like Momentum Strategy and Mean Revision. It consists of a variety of Machine Learning Techniques right from the basics ones like Decision Trees and K-Nearest Neighbors to advanced techniques like Gradient Boosted Classifiers and Random Forests.
What will you gain from this course?
- An understanding on developing Quants Trading models using advanced Machine Learning Techniques
- Knowledge on how to compare and evaluate strategies using Sharpe Ratios
- The ability to use approaches like Random Forests and K-Nearest Neighbors in developing Quant trading models
- Techniques to use Gradient Boosted trees and tune them to achieve a high performance
Prerequisites and Target Audience
Having basic knowledge of Machine Learning will make this course easier for you to understand. If you want to run the source code at the end of this course, you will require the basic knowledge of Python. This course is for:
- Analytics professionals big data professionals, and modelers who require/are looking to get a hands on experience with Machine Learning
- Quants traders who want to use Machine Learning techniques to develop trading strategies
- Anyone who wants to learn Machine Learning through a practical approach