5. Support Vector Machines
Linear SVM Classification
Soft Margin Classification
Nonlinear SVM Classification
Polynomial Kernel
Similarity Features
Gaussian RBF Kernel
Computational Complexity
SVM Regression
Under the Hood
Decision Function and Predictions
Training Objective
Quadratic Programming
The Dual Problem
Kernelized SVMs
Online SVMs
6. Decision Trees
Training and Visualizing a Decision Tree
Making Predictions
Estimating Class Probabilities
The CART Training Algorithm
Computational Complexity
Gini Impurity or Entropy?
Regularization Hyperparameters
Regression
Instability
Exercises
7. Ensemble Learning and Random Forests
Voting Classifiers
Bagging and Pasting
Bagging and Pasting in Scikit-Learn
Out-of-Bag Evaluation
Random Patches and Random Subspaces
Random Forests
Extra-Trees
Feature Importance
Boosting
AdaBoost
Gradient Boosting
Stacking
8. Dimensionality Reduction
The Curse of Dimensionality
Main Approaches for Dimensionality Reduction
Projection
Manifold Learning
PCA
Preserving the Variance
Principal Components
Projecting Down to d Dimensions
Using Scikit-Learn
Explained Variance Ratio
Choosing the Right Number of Dimensions
PCA for Compression
Randomized PCA
Incremental PCA
Selecting a Kernel and Tuning Hyperparameters
LLE
>Other Dimensionality Reduction Techniques
9. Unsupervised Learning Techniques
Clustering
K-Means
Limits of K-Means
Using Clustering for Image Segmentation
Using Clustering for Preprocessing
Using Clustering for Semi-Supervised Learning
DBSCAN
Other Clustering Algorithms
Gaussian Mixtures
Anomaly Detection Using Gaussian Mixtures
Selecting the Number of Clusters
Bayesian Gaussian Mixture Models
Other Algorithms for Anomaly and Novelty Detection