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Total Training – Machine Learning – Quant Trading
nnFinancial 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!nnWhat am I going to get from this course?nnDevelop Quant Trading models using advanced Machine Learning techniquesnCompare and evaluate strategies using Sharpe RatiosnUse techniques like Random Forests and K-Nearest Neighbors to develop Quant Trading modelsnUse Gradient Boosted trees and tune them for high performancenUse techniques like Feature engineering, parameter tuning and avoiding overfittingnBuild an end-to-end application from data collection and preparation to model selectionnnCourse Requirements:nnWorking knowledge of Python is necessary if you want to run the source code that is provided.nBasic knowledge of machine learning, especially Machine Learning classification techniques, would be helpful but it’s not mandatory.nnWhat is the target audience?nnGet Total Training – Machine Learning – Quant Trading downloadnnQuant traders who have not used Machine learning techniques before to develop trading strategiesnAnalytics professionals, modelers, big data professionals who want to get hands-on experience with Machine LearningnAnyone who is interested in Machine Learning and wants to learn through a practical, project-based approachTrading foreign exchange and algorithmic assets on margin carries a high level of risk and may not be suitable for all investors. Past performance does not guarantee future results.
