Machine Learning

  1. Supervised Learning
    1. Regression Analysis
      1. Linear Regression
        1. Simple Linear Regression
          1. Model Assumptions
            1. Least Squares Method
              1. Coefficient Interpretation
              2. Multiple Linear Regression
                1. Multiple Predictors
                  1. Coefficient Interpretation
                    1. Multicollinearity
                    2. Model Assumptions
                      1. Linearity
                        1. Independence
                          1. Homoscedasticity
                            1. Normality of Residuals
                            2. Cost Function
                              1. Mean Squared Error
                                1. Sum of Squared Residuals
                                2. Optimization Methods
                                  1. Analytical Solution
                                    1. Gradient Descent
                                      1. Stochastic Gradient Descent
                                      2. Model Evaluation
                                        1. Residual Analysis
                                          1. R-Squared
                                            1. Adjusted R-Squared
                                              1. F-Statistic
                                            2. Polynomial Regression
                                              1. Polynomial Features
                                                1. Model Complexity
                                                  1. Overfitting Issues
                                                    1. Cross-Validation for Model Selection
                                                    2. Regularization Techniques
                                                      1. Ridge Regression
                                                        1. L2 Penalty
                                                          1. Bias-Variance Tradeoff
                                                            1. Hyperparameter Tuning
                                                            2. Lasso Regression
                                                              1. L1 Penalty
                                                                1. Feature Selection Property
                                                                  1. Sparse Solutions
                                                                  2. Elastic Net
                                                                    1. Combined L1 and L2 Penalties
                                                                      1. Mixing Parameter
                                                                      2. Regularization Path
                                                                        1. Cross-Validation for Regularization
                                                                        2. Non-Linear Regression
                                                                          1. Basis Functions
                                                                            1. Kernel Methods
                                                                              1. Spline Regression
                                                                              2. Robust Regression
                                                                                1. Outlier-Resistant Methods
                                                                                  1. Huber Loss
                                                                                    1. Quantile Regression
                                                                                  2. Classification Methods
                                                                                    1. Logistic Regression
                                                                                      1. Binary Classification
                                                                                        1. Sigmoid Function
                                                                                          1. Odds and Log-Odds
                                                                                            1. Maximum Likelihood Estimation
                                                                                            2. Multinomial Classification
                                                                                              1. Softmax Function
                                                                                                1. One-vs-Rest
                                                                                                  1. One-vs-One
                                                                                                  2. Cost Function
                                                                                                    1. Cross-Entropy Loss
                                                                                                      1. Log-Likelihood
                                                                                                      2. Optimization
                                                                                                        1. Gradient Descent
                                                                                                          1. Newton-Raphson Method
                                                                                                          2. Model Interpretation
                                                                                                            1. Coefficient Interpretation
                                                                                                              1. Odds Ratios
                                                                                                              2. Regularization in Logistic Regression
                                                                                                                1. L1 Regularization
                                                                                                                  1. L2 Regularization
                                                                                                                2. k-Nearest Neighbors
                                                                                                                  1. Algorithm Mechanics
                                                                                                                    1. Distance Metrics
                                                                                                                      1. Euclidean Distance
                                                                                                                        1. Manhattan Distance
                                                                                                                          1. Minkowski Distance
                                                                                                                            1. Hamming Distance
                                                                                                                            2. Choosing k
                                                                                                                              1. Odd vs. Even k
                                                                                                                                1. Cross-Validation for k Selection
                                                                                                                                2. Weighted k-NN
                                                                                                                                  1. Computational Complexity
                                                                                                                                    1. Curse of Dimensionality
                                                                                                                                      1. Efficient Implementations
                                                                                                                                        1. KD-Trees
                                                                                                                                          1. Ball Trees
                                                                                                                                            1. LSH
                                                                                                                                          2. Support Vector Machines
                                                                                                                                            1. Linear SVM
                                                                                                                                              1. Maximum Margin Principle
                                                                                                                                                1. Support Vectors
                                                                                                                                                  1. Hard Margin Classification
                                                                                                                                                    1. Soft Margin Classification
                                                                                                                                                      1. Slack Variables
                                                                                                                                                      2. Non-Linear SVM
                                                                                                                                                        1. Kernel Trick
                                                                                                                                                          1. Kernel Functions
                                                                                                                                                            1. Linear Kernel
                                                                                                                                                              1. Polynomial Kernel
                                                                                                                                                                1. RBF Kernel
                                                                                                                                                                  1. Sigmoid Kernel
                                                                                                                                                                  2. Kernel Selection
                                                                                                                                                                  3. SVM for Regression
                                                                                                                                                                    1. Support Vector Regression
                                                                                                                                                                      1. Epsilon-Insensitive Loss
                                                                                                                                                                      2. Hyperparameter Tuning
                                                                                                                                                                        1. C Parameter
                                                                                                                                                                          1. Gamma Parameter
                                                                                                                                                                            1. Kernel Parameters
                                                                                                                                                                            2. Computational Considerations
                                                                                                                                                                              1. Quadratic Programming
                                                                                                                                                                                1. SMO Algorithm
                                                                                                                                                                              2. Naive Bayes Classifiers
                                                                                                                                                                                1. Bayes' Theorem in Classification
                                                                                                                                                                                  1. Naive Independence Assumption
                                                                                                                                                                                    1. Types of Naive Bayes
                                                                                                                                                                                      1. Gaussian Naive Bayes
                                                                                                                                                                                        1. Multinomial Naive Bayes
                                                                                                                                                                                          1. Bernoulli Naive Bayes
                                                                                                                                                                                            1. Complement Naive Bayes
                                                                                                                                                                                            2. Laplace Smoothing
                                                                                                                                                                                              1. Advantages and Limitations
                                                                                                                                                                                                1. Text Classification Applications
                                                                                                                                                                                                2. Decision Trees
                                                                                                                                                                                                  1. Tree Structure
                                                                                                                                                                                                    1. Root Node
                                                                                                                                                                                                      1. Internal Nodes
                                                                                                                                                                                                        1. Leaf Nodes
                                                                                                                                                                                                          1. Branches
                                                                                                                                                                                                          2. Splitting Criteria
                                                                                                                                                                                                            1. Information Gain
                                                                                                                                                                                                              1. Entropy
                                                                                                                                                                                                                1. Gini Impurity
                                                                                                                                                                                                                  1. Gain Ratio
                                                                                                                                                                                                                    1. Chi-Square
                                                                                                                                                                                                                    2. Tree Construction Algorithms
                                                                                                                                                                                                                      1. ID3
                                                                                                                                                                                                                        1. C4.5
                                                                                                                                                                                                                          1. CART
                                                                                                                                                                                                                          2. Handling Different Data Types
                                                                                                                                                                                                                            1. Categorical Features
                                                                                                                                                                                                                              1. Numerical Features
                                                                                                                                                                                                                                1. Missing Values
                                                                                                                                                                                                                                2. Pruning Techniques
                                                                                                                                                                                                                                  1. Pre-Pruning
                                                                                                                                                                                                                                    1. Post-Pruning
                                                                                                                                                                                                                                      1. Cost Complexity Pruning
                                                                                                                                                                                                                                      2. Advantages and Limitations
                                                                                                                                                                                                                                        1. Interpretability
                                                                                                                                                                                                                                          1. Non-Linear Relationships
                                                                                                                                                                                                                                            1. Overfitting Tendency
                                                                                                                                                                                                                                          2. Linear Discriminant Analysis
                                                                                                                                                                                                                                            1. Fisher's Linear Discriminant
                                                                                                                                                                                                                                              1. Assumptions
                                                                                                                                                                                                                                                1. Dimensionality Reduction
                                                                                                                                                                                                                                                  1. Comparison with PCA
                                                                                                                                                                                                                                                  2. Quadratic Discriminant Analysis
                                                                                                                                                                                                                                                    1. Relaxed Assumptions
                                                                                                                                                                                                                                                      1. Quadratic Decision Boundaries

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                                                                                                                                                                                                                                                  3. Data Preprocessing and Feature Engineering

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                                                                                                                                                                                                                                                  5. Unsupervised Learning