Supervised Learning - Comprehensive Tutorial (Python-based)
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
- 1.1.2. Ridge Regression
- 1.1.3. Lasso
- 1.1.4. Elastic Net
- 1.1.5. Multi-task Lasso
- 1.1.6. Least Angle Regression
- 1.1.7. LARS Lasso
- 1.1.8. Orthogonal Matching Pursuit (OMP)
- 1.1.9. Bayesian Regression
- 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
- 1.1.15. Polynomial regression: extending linear models with basis f...
- 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
- 1.11.1. Bagging meta-estimator
- 1.11.2. Forests of randomized trees
- 1.11.3. AdaBoost
- 1.11.4. Gradient Tree Boosting
- 1.11.5. VotingClassifier
- 1.12. Multiclass and multilabel algorithms
- 1.13. Feature selection
- 1.14. Semi-Supervised
- 1.15. Isotonic regression
- 1.16. Probability calibration
You can check all this information, here.
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