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 investment without proper care and considerations to the nuances of financial 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, professional Quant work flow, and much more. This course not only covers machine learning techniques, it also covers in depth the rationale of investing strategy 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 Algorithmic Trading Clustering and PCA for Investing If you are looking for a course on applying machine learning to investing, the Machine Learning for Finance and Algorithmic Trading & Investing Series is for you. With over 30 machine learning techniques test cases, which included popular 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 investing quickly.