This article is from Scikits learn. Scikit-learn Machine Learning in Python is simple and efficient tools for data mining and data analysis. Accessible to everybody, and reusable in various contexts. Built on NumPy, SciPy, and matplotlib. Open source, commercially usable - BSD license. 

• 1.1. Generalized Linear Models
  • 1.1.1. Ordinary Least Squares
    • 1.1.1.1. Ordinary Least Squares Complexity
  • 1.1.2. Ridge Regression
    • 1.1.2.1. Ridge Complexity
    • 1.1.2.2. Setting the regularization parameter: generalized Cross-Va...
  • 1.1.3. Lasso
    • 1.1.3.1. Setting regularization parameter
      • 1.1.3.1.1. Using cross-validation
      • 1.1.3.1.2. Information-criteria based model selection
  • 1.1.4. Elastic Net
  • 1.1.5. Multi-task Lasso
  • 1.1.6. Least Angle Regression
  • 1.1.7. LARS Lasso
    • 1.1.7.1. Mathematical formulation
  • 1.1.8. Orthogonal Matching Pursuit (OMP)
  • 1.1.9. Bayesian Regression
    • 1.1.9.1. Bayesian Ridge Regression
    • 1.1.9.2. Automatic Relevance Determination - ARD
  • 1.1.10. Logistic regression
  • 1.1.11. Stochastic Gradient Descent - SGD
  • 1.1.12. Perceptron
  • 1.1.13. Passive Aggressive Algorithms
  • 1.1.14. Robustness regression: outliers and modeling errors
    • 1.1.14.1. Different scenario and useful concepts
    • 1.1.14.2. RANSAC: RANdom SAmple Consensus
      • 1.1.14.2.1. Details of the algorithm
    • 1.1.14.3. Theil-Sen estimator: generalized-median-based estimator
      • 1.1.14.3.1. Theoretical considerations
  • 1.1.15. Polynomial regression: extending linear models with basis f...
• 1.2. Linear and Quadratic Discriminant Analysis
  • 1.2.1. Dimensionality reduction using Linear Discriminant Analysis
  • 1.2.2. Mathematical formulation of the LDA and QDA classifiers
  • 1.2.3. Mathematical formulation of LDA dimensionality reduction
  • 1.2.4. Shrinkage
  • 1.2.5. Estimation algorithms
• 1.3. Kernel ridge regression
• 1.4. Support Vector Machines
  • 1.4.1. Classification
    • 1.4.1.1. Multi-class classification
    • 1.4.1.2. Scores and probabilities
    • 1.4.1.3. Unbalanced problems
  • 1.4.2. Regression
  • 1.4.3. Density estimation, novelty detection
  • 1.4.4. Complexity
  • 1.4.5. Tips on Practical Use
  • 1.4.6. Kernel functions
    • 1.4.6.1. Custom Kernels
      • 1.4.6.1.1. Using Python functions as kernels
      • 1.4.6.1.2. Using the Gram matrix
      • 1.4.6.1.3. Parameters of the RBF Kernel
  • 1.4.7. Mathematical formulation
    • 1.4.7.1. SVC
    • 1.4.7.2. NuSVC
    • 1.4.7.3. SVR
  • 1.4.8. Implementation details
• 1.5. Stochastic Gradient Descent
  • 1.5.1. Classification
  • 1.5.2. Regression
  • 1.5.3. Stochastic Gradient Descent for sparse data
  • 1.5.4. Complexity
  • 1.5.5. Tips on Practical Use
  • 1.5.6. Mathematical formulation
    • 1.5.6.1. SGD
  • 1.5.7. Implementation details
• 1.6. Nearest Neighbors
  • 1.6.1. Unsupervised Nearest Neighbors
    • 1.6.1.1. Finding the Nearest Neighbors
    • 1.6.1.2. KDTree and BallTree Classes
  • 1.6.2. Nearest Neighbors Classification
  • 1.6.3. Nearest Neighbors Regression
  • 1.6.4. Nearest Neighbor Algorithms
    • 1.6.4.1. Brute Force
    • 1.6.4.2. K-D Tree
    • 1.6.4.3. Ball Tree
    • 1.6.4.4. Choice of Nearest Neighbors Algorithm
    • 1.6.4.5. Effect of leaf_size
  • 1.6.5. Nearest Centroid Classifier
    • 1.6.5.1. Nearest Shrunken Centroid
  • 1.6.6. Approximate Nearest Neighbors
    • 1.6.6.1. Locality Sensitive Hashing Forest
    • 1.6.6.2. Mathematical description of Locality Sensitive Hashing
• 1.7. Gaussian Processes
  • 1.7.1. Examples
    • 1.7.1.1. An introductory regression example
    • 1.7.1.2. Fitting Noisy Data
  • 1.7.2. Mathematical formulation
    • 1.7.2.1. The initial assumption
    • 1.7.2.2. The best linear unbiased prediction (BLUP)
    • 1.7.2.3. The empirical best linear unbiased predictor (EBLUP)
  • 1.7.3. Correlation Models
  • 1.7.4. Regression Models
  • 1.7.5. Implementation details
• 1.8. Cross decomposition
• 1.9. Naive Bayes
  • 1.9.1. Gaussian Naive Bayes
  • 1.9.2. Multinomial Naive Bayes
  • 1.9.3. Bernoulli Naive Bayes
  • 1.9.4. Out-of-core naive Bayes model fitting
• 1.10. Decision Trees
  • 1.10.1. Classification
  • 1.10.2. Regression
  • 1.10.3. Multi-output problems
  • 1.10.4. Complexity
  • 1.10.5. Tips on practical use
  • 1.10.6. Tree algorithms: ID3, C4.5, C5.0 and CART
  • 1.10.7. Mathematical formulation
    • 1.10.7.1. Classification criteria
    • 1.10.7.2. Regression criteria
• 1.11. Ensemble methods
  • 1.11.1. Bagging meta-estimator
  • 1.11.2. Forests of randomized trees
    • 1.11.2.1. Random Forests
    • 1.11.2.2. Extremely Randomized Trees
    • 1.11.2.3. Parameters
    • 1.11.2.4. Parallelization
    • 1.11.2.5. Feature importance evaluation
    • 1.11.2.6. Totally Random Trees Embedding
  • 1.11.3. AdaBoost
    • 1.11.3.1. Usage
  • 1.11.4. Gradient Tree Boosting
    • 1.11.4.1. Classification
    • 1.11.4.2. Regression
    • 1.11.4.3. Fitting additional weak-learners
    • 1.11.4.4. Controlling the tree size
    • 1.11.4.5. Mathematical formulation
      • 1.11.4.5.1. Loss Functions
    • 1.11.4.6. Regularization
      • 1.11.4.6.1. Shrinkage
      • 1.11.4.6.2. Subsampling
    • 1.11.4.7. Interpretation
      • 1.11.4.7.1. Feature importance
      • 1.11.4.7.2. Partial dependence
  • 1.11.5. VotingClassifier
    • 1.11.5.1. Majority Class Labels (Majority/Hard Voting)
      • 1.11.5.1.1. Usage
    • 1.11.5.2. Weighted Average Probabilities (Soft Voting)
    • 1.11.5.3. Using the VotingClassifier with GridSearch
      • 1.11.5.3.1. Usage
• 1.12. Multiclass and multilabel algorithms
  • 1.12.1. Multilabel classification format
  • 1.12.2. One-Vs-The-Rest
    • 1.12.2.1. Multiclass learning
    • 1.12.2.2. Multilabel learning
  • 1.12.3. One-Vs-One
    • 1.12.3.1. Multiclass learning
  • 1.12.4. Error-Correcting Output-Codes
    • 1.12.4.1. Multiclass learning
• 1.13. Feature selection
  • 1.13.1. Removing features with low variance
  • 1.13.2. Univariate feature selection
  • 1.13.3. Recursive feature elimination
  • 1.13.4. Feature selection using SelectFromModel
    • 1.13.4.1. L1-based feature selection
    • 1.13.4.2. Randomized sparse models
    • 1.13.4.3. Tree-based feature selection
  • 1.13.5. Feature selection as part of a pipeline
• 1.14. Semi-Supervised
  • 1.14.1. Label Propagation
• 1.15. Isotonic regression
• 1.16. Probability calibration

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