Summary Introduction ML101 Performance Metrics Bias and variance L1/L2 MLE/MAP/Bayesian Method Generative Model and Discriminative Models Preprocessing Numeric Features Categorical and Ordinal Features Datetime and Coordinates Handling Missing Values Meta algorithms Adaboost Gradient Boosting Decision Tree Meta Algorithms Deep Learning Basics CNN CNN_blocks Classic networks Data agumentation Frameworks Pandas Andrew Ng's guideline Tensorflow PyTorch Keras Classification Tree C4.5/CART RandomForests Logistic Regression SVM Regression test Clustering test Code code