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fet_Scikit-learn_Getting_Started
1. Supervised learning
2. Unsupervised learning
4. Inspection
5. Visualizations
6. Dataset transformations
7. Dataset loading utilities
8. Computing with scikit-learn
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1. Supervised learning
1.1. Linear Models
1.2. Linear and Quadratic Discriminant Analysis
1.3. Kernel ridge regression
1.4. Support Vector Machines
1.5. Stochastic Gradient Descent
1.6. Nearest Neighbors
1.7. Gaussian Processes
1.8. Cross decomposition
1.9. Naive Bayes
1.10. Decision Trees
1.11. Ensemble methods
1.12. Multiclass and multilabel algorithms
1.13. Feature selection
1.14. Semi-Supervised
1.15. Isotonic regression
1.16. Probability calibration
1.17. Neural network models (supervised)
1.14. Semi-Supervised
1.14. Semi-Supervised
1.14.1. Label Propagation
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