Classification-Based Machine Learning for Finance

Course details

Finally, a comprehensive hands-onmachine learningcourse with specific focus on classification based modelsfor the investmentcommunity and 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 and get you started in this space.

In this course, we are first going to provide some background information to machine learning. To ease you into the machine learning lingo, we start will something that most people are familiar with Logistic 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.

After covering the basics of classification based machine learning using logistic regression, we then move on to more advanced topics covering other classification machine learning algorithms such as Linear Discriminant Analysis, Quadratic Discriminant Analysis, Stochastic Gradient Descent classifier, Nearest Neighbors, Gaussian Naive Bayes and many more. We follow the foundations that we started in the first regression based machine learning course covering cross-validation, model validation,back test, professionalQuant work flow, and much more.

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

This course is the second 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, theMachine Learning for Finance and Algorithmic Trading & Investing Seriesisfor 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 applyingMachine Learningto investingquickly.