Scikit learn sklearn 0.18 official document Chinese version

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scikit-learn 0.18 Chinese document

• Quick start
• User guide
  • Supervised learning
    • Generalized Linear Models ( Generalized linear model )
    • Linear and Quadratic Discriminant Analysis ( Linear and quadratic discriminant analysis )
    • Kernel ridge regression ( Kernel ridge return )
    • Support Vector Machines（ Support vector machine , SVM）
    • Stochastic Gradient Descent ( Stochastic gradient descent )
    • Nearest Neighbors ( Nearest neighbor )
    • Gaussian Processes（ Gauss process ）
    • Cross decomposition（ Cross decomposition ）
    • Naive Bayes ( Naive Bayes )
    • Decision Trees（ Decision tree ）
    • Ensemble methods（ Integration method ）
    • Multi class and multi label algorithms
    • Functional selection
    • Semi supervision
    • Equation regression
    • Probability calibration
    • Neural network model （ supervise ）
  • Unsupervised learning
    • Gaussian mixture model
    • Manifold learning
    • clustering
    • BiCluster
    • Break down the signals in the ingredients （ Matrix decomposition problem ）
    • Covariance estimate
    • Novelty and anomaly detection
    • Density estimation
    • Neural network model （ Unsupervised ）
  • Model selection and evaluation
    • Cross validation ： Evaluate the performance of the estimator
    • Adjust the super parameters of the estimator
    • Model to evaluate ： Quantify the quality of the forecast
    • Model persistence
    • Verify the curve ： Draw scores to evaluate the model
  • Dataset conversion
    • Pipes and FeatureUnion： Combinatorial estimation
    • feature extraction
    • Preprocessing data
    • Unsupervised dimension reduction
    • Random projection
    • Kernel approximation
    • Pairwise measurement , Affinity and kernel
    • Convert forecast target （y）
  • Dataset loading utility
  • Computing strategy ： Bigger data
  • Computing performance
• course
• API file
• Development of guidelines
• flow chart
• Related packages
• Always ask questions
• Other resources
• Other versions

Learning route :  Fundamentals of machine learning  –> sklearn –> tensorflow（ Before learning this , We have to finish the foundation of big data first , The learning activity will be organized later , expect ing...）

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