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