Machine Learning with Python

  1. Supervised Learning Algorithms
    1. Linear Models
      1. Linear Regression
        1. Simple Linear Regression
          1. Least Squares Method
            1. Assumptions
              1. Interpretation
              2. Multiple Linear Regression
                1. Multiple Predictors
                  1. Multicollinearity
                    1. Feature Interactions
                    2. Polynomial Regression
                      1. Polynomial Features
                        1. Degree Selection
                          1. Overfitting Prevention
                        2. Regularized Linear Models
                          1. Ridge Regression
                            1. L2 Regularization
                              1. Ridge Parameter Selection
                                1. Bias-Variance Impact
                                2. Lasso Regression
                                  1. L1 Regularization
                                    1. Feature Selection Property
                                      1. Lasso Path
                                      2. Elastic Net
                                        1. Combined Regularization
                                          1. Parameter Balancing
                                        2. Logistic Regression
                                          1. Binary Classification
                                            1. Logistic Function
                                              1. Maximum Likelihood
                                                1. Odds Ratios
                                                2. Multiclass Classification
                                                  1. One-vs-Rest
                                                    1. One-vs-One
                                                      1. Multinomial Logistic Regression
                                                      2. Regularized Logistic Regression
                                                        1. L1 and L2 Penalties
                                                          1. Class Imbalance Handling
                                                      3. Tree-Based Models
                                                        1. Decision Trees
                                                          1. Tree Construction
                                                            1. Splitting Criteria
                                                              1. Information Gain
                                                                1. Gini Impurity
                                                                  1. Entropy
                                                                  2. Tree Pruning
                                                                    1. Pre-pruning
                                                                      1. Post-pruning
                                                                        1. Cost Complexity Pruning
                                                                        2. Tree Interpretation
                                                                          1. Feature Importance
                                                                            1. Decision Paths
                                                                              1. Tree Visualization
                                                                            2. Ensemble Methods
                                                                              1. Bagging
                                                                                1. Bootstrap Aggregating
                                                                                  1. Variance Reduction
                                                                                    1. Out-of-Bag Evaluation
                                                                                    2. Random Forests
                                                                                      1. Random Feature Selection
                                                                                        1. Forest Construction
                                                                                          1. Feature Importance
                                                                                            1. Hyperparameter Tuning
                                                                                            2. Extra Trees
                                                                                              1. Extremely Randomized Trees
                                                                                                1. Random Thresholds
                                                                                                2. Boosting
                                                                                                  1. AdaBoost
                                                                                                    1. Adaptive Boosting
                                                                                                      1. Weight Updates
                                                                                                        1. Weak Learners
                                                                                                        2. Gradient Boosting
                                                                                                          1. Gradient Descent Approach
                                                                                                            1. Residual Learning
                                                                                                              1. Shrinkage
                                                                                                              2. XGBoost
                                                                                                                1. Extreme Gradient Boosting
                                                                                                                  1. Regularization
                                                                                                                    1. Parallel Processing
                                                                                                                    2. LightGBM
                                                                                                                      1. Leaf-wise Growth
                                                                                                                        1. Categorical Features
                                                                                                                          1. Memory Efficiency
                                                                                                                          2. CatBoost
                                                                                                                            1. Categorical Boosting
                                                                                                                              1. Ordered Boosting
                                                                                                                                1. Symmetric Trees
                                                                                                                          3. Support Vector Machines
                                                                                                                            1. Linear SVM
                                                                                                                              1. Maximum Margin Principle
                                                                                                                                1. Support Vectors
                                                                                                                                  1. Hard Margin SVM
                                                                                                                                    1. Soft Margin SVM
                                                                                                                                      1. Hinge Loss
                                                                                                                                      2. Non-linear SVM
                                                                                                                                        1. Kernel Trick
                                                                                                                                          1. Polynomial Kernels
                                                                                                                                            1. Radial Basis Function Kernels
                                                                                                                                              1. Sigmoid Kernels
                                                                                                                                                1. Custom Kernels
                                                                                                                                                2. SVM Implementation
                                                                                                                                                  1. Hyperparameter Tuning
                                                                                                                                                    1. Scaling Requirements
                                                                                                                                                      1. Multi-class SVM
                                                                                                                                                        1. Probability Estimates
                                                                                                                                                      2. Instance-Based Learning
                                                                                                                                                        1. K-Nearest Neighbors
                                                                                                                                                          1. Distance Metrics
                                                                                                                                                            1. Euclidean Distance
                                                                                                                                                              1. Manhattan Distance
                                                                                                                                                                1. Minkowski Distance
                                                                                                                                                                  1. Cosine Similarity
                                                                                                                                                                  2. Choosing K
                                                                                                                                                                    1. Odd vs Even K
                                                                                                                                                                      1. Cross-Validation Selection
                                                                                                                                                                        1. Bias-Variance Tradeoff
                                                                                                                                                                        2. Weighted KNN
                                                                                                                                                                          1. Distance Weighting
                                                                                                                                                                            1. Uniform Weighting
                                                                                                                                                                            2. Curse of Dimensionality
                                                                                                                                                                              1. High-Dimensional Challenges
                                                                                                                                                                                1. Dimensionality Reduction
                                                                                                                                                                            3. Probabilistic Models
                                                                                                                                                                              1. Naive Bayes
                                                                                                                                                                                1. Bayes' Theorem
                                                                                                                                                                                  1. Independence Assumption
                                                                                                                                                                                    1. Prior and Posterior Probabilities
                                                                                                                                                                                    2. Naive Bayes Variants
                                                                                                                                                                                      1. Gaussian Naive Bayes
                                                                                                                                                                                        1. Continuous Features
                                                                                                                                                                                          1. Normal Distribution Assumption
                                                                                                                                                                                          2. Multinomial Naive Bayes
                                                                                                                                                                                            1. Discrete Features
                                                                                                                                                                                              1. Text Classification
                                                                                                                                                                                              2. Bernoulli Naive Bayes
                                                                                                                                                                                                1. Binary Features
                                                                                                                                                                                                  1. Document Classification
                                                                                                                                                                                                2. Model Evaluation
                                                                                                                                                                                                  1. Laplace Smoothing
                                                                                                                                                                                                    1. Feature Selection
                                                                                                                                                                                                      1. Handling Zero Probabilities

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                                                                                                                                                                                                  5. Unsupervised Learning Algorithms