Useful Links
1. Foundations of Predictive Analytics
2. Data Foundation and Preparation
3. Regression Modeling
4. Classification Modeling
5. Ensemble Methods
6. Neural Networks and Deep Learning
7. Time Series Analysis and Forecasting
8. Unsupervised Learning
9. Model Evaluation and Validation
10. Model Interpretability and Explainability
11. Model Deployment and Production
12. Business Applications and Use Cases
13. Ethics and Responsible AI
  1. Computer Science
  2. Data Science

Predictive Analytics

1. Foundations of Predictive Analytics
2. Data Foundation and Preparation
3. Regression Modeling
4. Classification Modeling
5. Ensemble Methods
6. Neural Networks and Deep Learning
7. Time Series Analysis and Forecasting
8. Unsupervised Learning
9. Model Evaluation and Validation
10. Model Interpretability and Explainability
11. Model Deployment and Production
12. Business Applications and Use Cases
13. Ethics and Responsible AI
  1. Model Interpretability and Explainability
    1. Interpretability Fundamentals
      1. Global vs Local Interpretability
        1. Model-specific vs Model-agnostic Methods
          1. Interpretability vs Accuracy Tradeoff
          2. Intrinsically Interpretable Models
            1. Linear Models
              1. Coefficient Interpretation
                1. Feature Importance
                2. Decision Trees
                  1. Rule Extraction
                    1. Path Analysis
                    2. Rule-based Models
                      1. Decision Rules
                        1. Association Rules
                      2. Model-agnostic Explanation Methods
                        1. Permutation Feature Importance
                          1. Feature Shuffling
                            1. Importance Ranking
                            2. Partial Dependence Plots
                              1. Marginal Effects
                                1. Interaction Effects
                                2. LIME
                                  1. Local Linear Approximation
                                    1. Instance-specific Explanations
                                      1. Perturbation Strategies
                                      2. SHAP
                                        1. Shapley Value Theory
                                          1. Additive Feature Attribution
                                            1. SHAP Value Calculation
                                              1. Visualization Techniques
                                            2. Global Explanation Techniques
                                              1. Feature Importance Rankings
                                                1. Model Summaries
                                                  1. Surrogate Models
                                                    1. Global Surrogate
                                                      1. Local Surrogate
                                                    2. Visualization for Interpretability
                                                      1. Feature Effect Plots
                                                        1. Interaction Plots
                                                          1. Decision Boundaries
                                                            1. Model Behavior Visualization

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                                                          9. Model Evaluation and Validation

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                                                          11. Model Deployment and Production

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