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

Confusion Matrix Anatomy Accuracy, Precision, Recall, F1 ROC Curves and AUC Precision–Recall Curves and AUC-PR Threshold Selection Strategies Cost Curves and Expected Utility Probability Calibration Methods Brier Score and Log Loss Multiclass Metrics and Averaging Ranking Metrics for Imbalanced Data Top-k and Coverage Metrics Macro vs Micro vs Weighted Scores Cumulative Gain and Lift Charts Calibration Plots and Reliability Decision Curves and Net Benefit

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

15Deployment, Monitoring, and Capstone Project

Courses/Supervised Machine Learning: Regression and Classification/Classification II: Thresholding, Calibration, and Metrics

Classification II: Thresholding, Calibration, and Metrics

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Make cost-aware decisions by selecting thresholds, calibrating probabilities, and using the right metrics.

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Probability Calibration Methods

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