1. Supervised learning

• 1.1. Linear Models
  • 1.1.2. Ridge regression and classification
  • 1.1.4. Multi-task Lasso
  • 1.1.6. Multi-task Elastic-Net
  • 1.1.8. LARS Lasso
  • 1.1.10. Bayesian Regression
  • 1.1.12. Stochastic Gradient Descent - SGD
  • 1.1.14. Passive Aggressive Algorithms
  • 1.1.16. Polynomial regression: extending linear models with basis functions
• • 1.2.1. Dimensionality reduction using Linear Discriminant Analysis
  • 1.2.3. Mathematical formulation of LDA dimensionality reduction
  • 1.2.5. Estimation algorithms
• 1.4. Support Vector Machines
  • 1.4.2. Regression
  • 1.4.4. Complexity
  • 1.4.6. Kernel functions
  • 1.4.8. Implementation details
• • 1.5.1. Classification
  • 1.5.4. Complexity
  • 1.5.6. Tips on Practical Use
  • 1.5.8. Implementation details
• • 1.6.1. Unsupervised Nearest Neighbors
  • 1.6.3. Nearest Neighbors Regression
  • 1.6.5. Nearest Centroid Classifier
  • 1.6.7. Neighborhood Components Analysis
• • 1.7.1. Gaussian Process Regression (GPR)
  • 1.7.3. Gaussian Process Classification (GPC)
  • 1.7.5. Kernels for Gaussian Processes
• 1.9. Naive Bayes
  • 1.9.2. Multinomial Naive Bayes
  • 1.9.4. Bernoulli Naive Bayes
  • 1.9.6. Out-of-core naive Bayes model fitting
• • 1.10.1. Classification
  • 1.10.3. Multi-output problems
  • 1.10.5. Tips on practical use
  • 1.10.7. Mathematical formulation
• 1.12. Multiclass and multilabel algorithms
  • 1.12.2. One-Vs-The-Rest
  • 1.12.4. Error-Correcting Output-Codes
  • 1.12.6. Multioutput classification
  • 1.12.8. Regressor Chain
• • 1.13.1. Removing features with low variance
  • 1.13.3. Recursive feature elimination
  • 1.13.5. Feature selection as part of a pipeline
• • 1.14.1. Label Propagation
• 1.16. Probability calibration
• • 1.17.1. Multi-layer Perceptron
  • 1.17.3. Regression
  • 1.17.5. Algorithms
  • 1.17.7. Mathematical formulation