1 Introduction to Machine Learning
2.7 Putting the processes together
3 Feature & Target Engineering
3.3.1 Visualizing missing values
3.5 Numeric feature engineering
3.6 Categorical feature engineering
3.6.2 One-hot & dummy encoding
3.8.3 Putting the process together
4.3 Multiple linear regression
4.6 Principal component regression
5.3 Simple logistic regression
5.4 Multiple logistic regression
7 Multivariate Adaptive Regression Splines
7.2.1 Multivariate adaptive regression splines
7.3 Fitting a basic MARS model
10.2 Why and when bagging works
11.3 Out-of-the-box performance
12.2.1 A sequential ensemble approach
12.3.3 General tuning strategy
12.4.1 Stochastic hyperparameters
12.5.1 XGBoost hyperparameters
14.2 Optimal separating hyperplanes
14.2.1 The hard margin classifier
14.2.2 The soft margin classifier
14.3 The support vector machine
14.3.2 Support vector regression
15.2.1 Common ensemble methods
15.2.2 Super learner algorithm
15.5 Automated machine learning
16 Interpretable Machine Learning
16.2.3 Model-specific vs. model-agnostic
16.3 Permutation-based feature importance
16.5 Individual conditional expectation
16.7 Local interpretable model-agnostic explanations
16.8.3 XGBoost and built-in Shapley values
16.9 Localized step-wise procedure
17 Principal Components Analysis
17.3 Finding principal components
17.5 Selecting the number of principal components
17.5.2 Proportion of variance explained criterion
18 Generalized Low Rank Models
18.3.1 Alternating minimization
18.4.2 Tuning to optimize for unseen data
19.2 Undercomplete autoencoders
19.2.1 Comparing PCA to an autoencoder
19.2.3 Visualizing the reconstruction
20.7 Clustering with mixed data
20.8 Alternative partitioning methods
21.2 Hierarchical clustering algorithms
21.3 Hierarchical clustering in R
21.3.1 Agglomerative hierarchical clustering
21.3.2 Divisive hierarchical clustering
21.4 Determining optimal clusters