SUMMARY.md

Summary

• 機器學習：使用Python
  • 簡介Scikit-learn 機器學習
• 分類法 Classification
  • Ex 1: Recognizing hand-written digits
  • EX 2: Normal and Shrinkage Linear Discriminant Analysis for classification
  • EX 3: Plot classification probability
  • EX 4: Classifier Comparison
  • EX 5: Linear and Quadratic Discriminant Analysis with confidence ellipsoid
• 特徵選擇 Feature Selection
  • Ex 1: Pipeline Anova SVM
  • Ex 2: Recursive Feature Elimination
  • Ex 3: Recursive Feature Elimination with Cross-Validation
  • Ex 4: Feature Selection using SelectFromModel
  • Ex 5: Test with permutations the significance of a classification score
  • Ex 6: Univariate Feature Selection
  • Ex 7: Comparison of F-test and mutual information
• 互分解 Cross Decomposition
• 通用範例 General Examples
  • Ex 1: Plotting Cross-Validated Predictions
  • Ex 2: Concatenating multiple feature extraction methods
  • Ex 3: Isotonic Regression
  • Ex 4: Imputing missing values before building an estimator
  • Ex 7: Face completion with a multi-output estimators
• 群聚法 Clustering
  • EX 12:Spectral clustering for image segmentation
• 機器學習資料集 Datasets
  • Ex 1: The digits 手寫數字辨識
  • Ex 3: The iris 鳶尾花資料集
• 應用範例 Application
  • 波士頓房地產雲端評估(一)
  • 波士頓房地產雲端評估(二)
• 類神經網路 Neural_Networks
  • Ex 1: Visualization of MLP weights on MNIST
  • Ex 2: Restricted Boltzmann Machine features for digit classification
  • Ex 3: Compare Stochastic learning strategies for MLPClassifier
  • Ex 4: Varying regularization in Multi-layer Perceptron
• 決策樹 Decision_trees
  • Ex 1: Decision Tree Regression
  • Ex 2: Multi-output Decision Tree Regression
  • Ex 3: Plot the decision surface of a decision tree on the iris dataset
  • Ex 4: Understanding the decision tree structure