Machine Learning with Scikit-Learn

  1. Supervised Learning: Regression
    1. Linear Models
      1. Simple Linear Regression
        1. Mathematical Foundation
          1. Assumptions
            1. Interpretation
            2. Multiple Linear Regression
              1. Multiple Predictors
                1. Coefficient Interpretation
                  1. Multicollinearity
                  2. Polynomial Regression
                    1. Non-linear Relationships
                      1. Degree Selection
                      2. Ridge Regression
                        1. L2 Regularization
                          1. Alpha Parameter Tuning
                            1. Bias-Variance Tradeoff
                            2. Lasso Regression
                              1. L1 Regularization
                                1. Feature Selection Effect
                                  1. Alpha Parameter Tuning
                                  2. ElasticNet
                                    1. Combined L1 and L2 Regularization
                                      1. L1 Ratio Parameter
                                        1. Parameter Tuning
                                        2. Logistic Regression for Regression
                                          1. Stochastic Gradient Descent Regressor
                                            1. Online Learning
                                              1. Loss Functions
                                                1. Learning Rate
                                              2. Support Vector Regression
                                                1. SVR Concept
                                                  1. Support Vectors in Regression
                                                    1. Epsilon Parameter
                                                      1. Kernel Functions
                                                        1. Linear Kernel
                                                          1. Polynomial Kernel
                                                            1. RBF Kernel
                                                              1. Sigmoid Kernel
                                                              2. Parameter Tuning
                                                                1. C Parameter
                                                                  1. Epsilon Parameter
                                                                    1. Kernel Parameters
                                                                  2. Tree-Based Models
                                                                    1. Decision Tree Regressor
                                                                      1. Tree Construction
                                                                        1. Splitting Criteria
                                                                          1. Pruning Techniques
                                                                            1. Feature Importance
                                                                            2. Ensemble Methods
                                                                              1. Random Forest Regressor
                                                                                1. Bootstrap Aggregating
                                                                                  1. Random Feature Selection
                                                                                    1. Out-of-Bag Error
                                                                                      1. Feature Importance
                                                                                      2. Extra Trees Regressor
                                                                                        1. Extremely Randomized Trees
                                                                                        2. Gradient Boosting Regressor
                                                                                          1. Sequential Learning
                                                                                            1. Learning Rate
                                                                                              1. Number of Estimators
                                                                                                1. Subsample Parameter
                                                                                                2. AdaBoost Regressor
                                                                                                  1. Adaptive Boosting
                                                                                                    1. Base Estimators
                                                                                                      1. Learning Rate
                                                                                                      2. Histogram-based Gradient Boosting
                                                                                                    2. K-Nearest Neighbors Regression
                                                                                                      1. Distance-Based Prediction
                                                                                                        1. Choosing K
                                                                                                          1. Distance Metrics
                                                                                                            1. Weighted Predictions
                                                                                                            2. Gaussian Process Regression
                                                                                                              1. Probabilistic Approach
                                                                                                                1. Kernel Functions
                                                                                                                  1. Uncertainty Quantification
                                                                                                                  2. Neural Network Models
                                                                                                                    1. MLPRegressor
                                                                                                                      1. Multi-layer Perceptron
                                                                                                                        1. Hidden Layers
                                                                                                                          1. Activation Functions
                                                                                                                            1. Solver Options

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                                                                                                                        4. Data Preprocessing and Feature Engineering

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                                                                                                                        6. Supervised Learning: Classification