Free graduate level course: Introduction to Machine Learning

"I thought the course was fantastic! This was my first Machine Learning course, and I felt it touched on all of the topics I would have hoped."

*Key learning algorithms from the ground up*: Logistic Regression, Naive Bayes, Decision Trees, Random Forests, Boosting, Bagging, Stacking, Clustering, Neural Networks, and Reinforcement Learning.

*Professional model building techniques*: evaluation, ROC curves, statistical bounds, bias and variance, dealing with mistakes, tuning hyperparameters, and feature engineering for text and graphics.

*Fundamentals of machine learning engineering*: when to use ML, intelligent user experiences, orchestrating ML systems, avoiding bias in machine learning, and case studies from Internet scale ML systems.