Regression-Based Machine Learning for Algorithmic Trading

Course details

Finally, a comprehensive hands-onmachine learningcourse with specific focus on regression based modelsfor the investmentcommunity and any passionate investors.

In the past few years, there has been a massive adoptionand growth in the use of data science, artificial intelligence and machine learning to find alpha. However, information onand application of machine learning to investment are scarce. This course has been designed to address that. It is meant to spark your creative juices.

In this course, we are first going to provide some background information to machine learning. To ease you into the machine lingo, we start will something that most people are familiar with - Linear Regression. The assumptions of financial time series as well as the stylized facts are introduced and explained at length due to its importance. The assumptions of linear regression are also highlighted to demonstrate the challenges and danger of blindly applying machine learning to investmentwithout proper care and considerations to the nuances offinancial time series.

More advanced topics of cross-validation, model validation,penalized regression - Lasso, Ridge, and ElasticNet, Kalman Filter, back test, professionalQuant work flow, cross-sectional and time-series momentum are also explain in details.

This course not onlycovers machine learning techniques, it also coversin depth the rationale of investingstrategy development.

This course is the first of the Machine Learning for Finance and Algorithmic Trading & Investing Series. The courses in the series includes:

• Regression-Based Machine Learning for Algorithmic Trading
• Classification-Based Machine Learning for Algorithmic Trading
• Ensemble Machine Learning for Algorithmic Trading
• Unsupervised Machine Learning: Hidden Markov for AlgorithmicTrading
• Clustering and PCA for Investing

If you are looking for a course on applyingmachine learning to investing, the Machine Learning for Finance and Algorithmic Trading & Investing Series isfor you. With over 30 machine learning techniques test cases, which includedpopular techniques such as Lasso regression, Ridge regression,SVM, XGBoost, random forest, Hidden Markov Model, common clustering techniques and many more,to get you started with applying Machine Learning to investingquickly.