- 1. Installing scikits.learn
- 2. Getting started: an introduction to machine learning with scikits.learn
- 3. Supervised learning
- 3.1. Generalized Linear Models
- 3.2. Support Vector Machines
- 3.2.1. Classification
- 3.2.2. Regression
- 3.2.3. Density estimation, outliers detection
- 3.2.4. Support Vector machines for sparse data
- 3.2.5. Complexity
- 3.2.6. Tips on Practical Use
- 3.2.7. Kernel functions
- 3.2.8. Mathematical formulation
- 3.2.9. Frequently Asked Questions
- 3.2.10. Implementation details
- 3.3. Stochastic Gradient Descent
- 3.4. Nearest Neighbors
- 3.5. Feature selection
- 3.6. Gaussian Processes
- 4. Unsupervised learning
- 5. Model Selection
- 6. Class Reference
- 6.1. Support Vector Machines
- 6.2. Generalized Linear Models
- 6.3. Bayesian Regression
- 6.4. Naive Bayes
- 6.5. Nearest Neighbors
- 6.6. Gaussian Mixture Models
- 6.7. Hidden Markov Models
- 6.8. Clustering
- 6.9. Covariance Estimators
- 6.10. Signal Decomposition
- 6.11. Cross Validation
- 6.12. Grid Search
- 6.13. Feature Selection
- 6.14. Feature Extraction
- 6.15. Pipeline