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  • 1. Supervised learning
  • 1.1. Linear Models
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Thursday, July 23, 2020

fet_INDEX_A_to_Z

fet_INDEX_A_to_Z

01. index_A

02. index_B

03. index_C

04. index_D

05. index_E

06. index_F

07. index_G

08. index_H

09. index_I

10. index_J

11. index_K

12. index_L

13. index_M

14. index_N

15. index_O

16. index_P

17. index_Q

18. index_R

19. index_S

20. index_T

21. index_U

22. index_V

23. index_W

24. index_X

25. index_Y

26. index_Z

Posted by fet_Faculty at 4:10 AM 1 comment:
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  • 1. Supervised learning
    • 1.1. 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. Metrics and scoring: quantifying the quality of predictions
    • 3.4. Model persistence
    • 3.5. Validation curves: plotting scores to evaluate models
  • 4. Inspection
    • 4.1. Partial dependence plots
    • 4.2. Permutation feature importance
  • 5. Visualizations
    • 5.1. Available Plotting Utilities
  • 6. Dataset transformations
    • 6.1. Pipelines and composite estimators
    • 6.2. Feature extraction
    • 6.3. Preprocessing data
    • 6.4. Imputation of missing values
    • 6.5. Unsupervised dimensionality reduction
    • 6.6. Random Projection
    • 6.7. Kernel Approximation
    • 6.8. Pairwise metrics, Affinities and Kernels
    • 6.9. Transforming the prediction target (y)
  • 7. Dataset loading utilities
    • 7.1. General dataset API
    • 7.2. Toy datasets
    • 7.3. Real world datasets
    • 7.4. Generated datasets
    • 7.5. Loading other datasets
  • 8. Computing with scikit-learn
    • 8.1. Strategies to scale computationally: bigger data
    • 8.2. Computational Performance
    • 8.3. Parallelism, resource management, and configuration

Blog Archive

  • ▼  2020 (1)
    • ▼  July (1)
      • fet_INDEX_A_to_Z

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fet_Faculty
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  • 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. Metrics and scoring: quantifying the quality of predictions
    • 3.4. Model persistence
    • 3.5. Validation curves: plotting scores to evaluate models
  • 4. Inspection
    • 4.1. Partial dependence plots
    • 4.2. Permutation feature importance
  • 5. Visualizations
    • 5.1. Available Plotting Utilities
  • 6. Dataset transformations
    • 6.1. Pipelines and composite estimators
    • 6.2. Feature extraction
    • 6.3. Preprocessing data
    • 6.4. Imputation of missing values
    • 6.5. Unsupervised dimensionality reduction
    • 6.6. Random Projection
    • 6.7. Kernel Approximation
    • 6.8. Pairwise metrics, Affinities and Kernels
    • 6.9. Transforming the prediction target (y)
  • 7. Dataset loading utilities
    • 7.1. General dataset API
    • 7.2. Toy datasets
    • 7.3. Real world datasets
    • 7.4. Generated datasets
    • 7.5. Loading other datasets
  • 8. Computing with scikit-learn
    • 8.1. Strategies to scale computationally: bigger data
    • 8.2. Computational Performance
    • 8.3. Parallelism, resource management, and configuration
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