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Supervised Machine Learning: Regression and Classification

Chapters

1Foundations of Supervised Learning

2Data Wrangling and Feature Engineering

3Exploratory Data Analysis for Predictive Modeling

4Train/Validation/Test and Cross-Validation Strategies

5Regression I: Linear Models

6Regression II: Regularization and Advanced Techniques

7Classification I: Logistic Regression and Probabilistic View

8Classification II: Thresholding, Calibration, and Metrics

9Distance- and Kernel-Based Methods

10Tree-Based Models and Ensembles

11Handling Real-World Data Issues

12Dimensionality Reduction and Feature Selection

13Model Tuning, Pipelines, and Experiment Tracking

14Model Interpretability and Responsible AI

Global vs Local Explanations Coefficient-Based Interpretation Permutation Importance Pitfalls SHAP Values for Trees and Linear Models LIME for Local Explanations Counterfactual Explanations Partial Dependence and ICE Best Practices Feature Interaction Analysis Monotonic Constraints in Models Detecting and Mitigating Bias Fairness Metrics and Trade-offs Privacy Risks in Supervised Models Adversarial Examples in Tabular Data Transparency and Documentation Human-in-the-Loop Review

15Deployment, Monitoring, and Capstone Project

Courses/Supervised Machine Learning: Regression and Classification/Model Interpretability and Responsible AI

Model Interpretability and Responsible AI

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Explain model behavior, assess fairness, and communicate uncertainty responsibly.

Content

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Monotonic Constraints in Models

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