Machine Learning Fundamentals

  1. Supervised Learning: Regression
    1. Concept of Regression: Predicting Continuous Values
      1. Use Cases and Examples
        1. Price Prediction
          1. Sales Forecasting
            1. Risk Assessment
              1. Performance Metrics
            2. Linear Regression
              1. Simple Linear Regression
                1. Model Equation
                  1. Slope and Intercept
                    1. Mathematical Formulation
                    2. Interpretation of Coefficients
                      1. Statistical Significance
                        1. Confidence Intervals
                          1. Effect Size
                          2. Assumptions Validation
                          3. Multiple Linear Regression
                            1. Multivariate Inputs
                              1. Feature Interactions
                                1. Model Complexity
                                2. Multicollinearity
                                  1. Detection Methods
                                    1. Variance Inflation Factor
                                      1. Mitigation Strategies
                                    2. Assumptions of Linear Regression
                                      1. Linearity
                                        1. Relationship Assessment
                                          1. Residual Plots
                                          2. Independence
                                            1. Autocorrelation Testing
                                              1. Durbin-Watson Test
                                              2. Homoscedasticity
                                                1. Constant Variance
                                                  1. Breusch-Pagan Test
                                                  2. Normality of Errors
                                                    1. Q-Q Plots
                                                      1. Shapiro-Wilk Test
                                                    2. Limitations of Linear Regression
                                                      1. Non-Linear Relationships
                                                        1. Outlier Sensitivity
                                                          1. Feature Scaling Requirements
                                                        2. Polynomial Regression
                                                          1. Model Equation
                                                            1. Degree Selection
                                                              1. Feature Transformation
                                                              2. Overfitting Risks
                                                                1. High-Degree Polynomials
                                                                  1. Regularization Needs
                                                                    1. Cross-Validation Assessment
                                                                  2. Regularized Regression
                                                                    1. Ridge Regression
                                                                      1. L2 Penalty
                                                                        1. Coefficient Shrinkage
                                                                        2. Lasso Regression
                                                                          1. L1 Penalty
                                                                            1. Feature Selection
                                                                            2. Elastic Net
                                                                              1. Combined Penalties
                                                                                1. Parameter Tuning
                                                                              2. Evaluating Regression Models
                                                                                1. Mean Absolute Error (MAE)
                                                                                  1. Interpretation
                                                                                    1. Robustness to Outliers
                                                                                    2. Mean Squared Error (MSE)
                                                                                      1. Penalty for Large Errors
                                                                                        1. Optimization Properties
                                                                                        2. Root Mean Squared Error (RMSE)
                                                                                          1. Scale Interpretation
                                                                                            1. Comparison with MAE
                                                                                            2. R-squared (Coefficient of Determination)
                                                                                              1. Variance Explained
                                                                                                1. Adjusted R-squared
                                                                                                  1. Limitations
                                                                                                  2. Residual Analysis
                                                                                                    1. Residual Plots
                                                                                                      1. Pattern Detection
                                                                                                        1. Assumption Validation

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                                                                                                    3. The End-to-End Machine Learning Workflow

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                                                                                                    5. Supervised Learning: Classification