In this course, you will :Regression analysis. K-Means Clustering. Principal Component Analysis. Train/Test and cross validation. Bayesian Methods. Decision Trees and Random Forests. Multivariate Regression. Multi-Level Models. Support Vector Machines. Reinforcement Learning. Collaborative Filtering. K-Nearest Neighbor. Bias/Variance Tradeoff. Ensemble Learning. Term Frequency / Inverse Document Frequency. Experimental Design and A/B Tests.