classification and regression generalizationoverfittingand underfitting relation of model complexity to dataset size supervised machine learning algorithms some sample datasets -nearest neighbors linear models naive bayes classifiers decision trees ensembles of decision trees kernelized support vector machines neural networks (deep learninguncertainty estimates from classifiers the decision function predicting probabilities uncertainty in multiclass classification summary and outlook unsupervised learning and preprocessing types of unsupervised learning challenges in unsupervised learning preprocessing and scaling different kinds of preprocessing applying data transformations scaling training and test data the same way the effect of preprocessing on supervised learning dimensionality reductionfeature extractionand manifold learning principal component analysis (pcanon-negative matrix factorization (nmfmanifold learning with -sne clustering -means clustering agglomerative clustering dbscan comparing and evaluating clustering algorithms summary of clustering methods summary and outlook representing data and engineering features categorical variables one-hot-encoding (dummy variablesiv table of contents