Machine Learning in Python @ sci-kit learn

• This repository provides the user guide of scikit learn translated into Korean.
• scikit learn 의 사용자 가이드를 한국어로 번역하여 제공하고 있습니다.
• Simply click on the links to view the original and translated page on active link as blue due to on-going project
• 진행중인 프로젝트인 관계로 파란색으로 활성화된 링크를 클릭하여 원본과 번역본을 확인하시기 바랍니다.

Table of Contents

• 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
  • 1.17. Neural network models (supervised)

• 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. Tuning the hyper-parameters of an estimator
  • 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. Loading from external datasets
  • 5.7. The Olivetti faces dataset
  • 5.8. The 20 newsgroups text dataset
  • 5.9. Downloading datasets from the mldata.org repository
  • 5.10. The Labeled Faces in the Wild face recognition dataset
  • 5.11. Forest covertypes
  • 5.12. RCV1 dataset
  • 5.13. Boston House Prices dataset
  • 5.14. Breast Cancer Wisconsin (Diagnostic) Database
  • 5.15. Diabetes dataset
  • 5.16. Optical Recognition of Handwritten Digits Data Set
  • 5.17. Iris Plants Database
  • 5.18. Linnerrud dataset

• 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