scikit-learn：0. user_guide——需要学习的所有内容

内容来自：http://scikit-learn.org/stable/index.html

• 1. Supervised learning
  • 1.1. Generalized 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
• 2. Unsupervised learning
  • 2.1. Gaussian mixture models
  • 2.2. Manifold learning
  • 2.3. Clustering
  • 2.4. Biclustering
  • 2.5. Decomposing signals in components (matrix factorization problems)
  • 2.6. Covariance estimation
  • 2.7. Novelty and Outlier Detection
  • 2.8. Density Estimation
  • 2.9. Neural network models (unsupervised)
• 3. Model selection and evaluation
  • 3.1. Cross-validation: evaluating estimator performance
  • 3.2. Grid Search: Searching for estimator parameters
  • 3.3. Model evaluation: quantifying the quality of predictions
  • 3.4. Model persistence
  • 3.5. Validation curves: plotting scores to evaluate models
• 4. Dataset transformations
  • 4.1. Pipeline and FeatureUnion: combining estimators
  • 4.2. Feature extraction
  • 4.3. Preprocessing data
  • 4.4. Unsupervised dimensionality reduction
  • 4.5. Random Projection
  • 4.6. Kernel Approximation
  • 4.7. Pairwise metrics, Affinities and Kernels
  • 4.8. Transforming the prediction target (y)
• 5. Dataset loading utilities
  • 5.1. General dataset API
  • 5.2. Toy datasets
  • 5.3. Sample images
  • 5.4. Sample generators
  • 5.5. Datasets in svmlight / libsvm format
  • 5.6. The Olivetti faces dataset
  • 5.7. The 20 newsgroups text dataset
  • 5.8. Downloading datasets from the mldata.org repository
  • 5.9. The Labeled Faces in the Wild face recognition dataset
  • 5.10. Forest covertypes
• 6. Strategies to scale computationally: bigger data
  • 6.1. Scaling with instances using out-of-core learning
• 7. Computational Performance
  • 7.1. Prediction Latency
  • 7.2. Prediction Throughput
  • 7.3. Tips and Tricks