Introduction to Machine Learning
This course builds an essential toolkit for anyone starting out in ML or data science. Foundational issues in this area, such as cross-validation and the bias-variance trade-off, are covered with a focus on the intuition behind their use. This course also explores the principal techniques that any machine learner or data scientist should know including logistic regression, decision trees, classification and clustering.
Participants are expected to be proficient in python (at least one course) with basic statistics knowledge being helpful but not required.