1
Supervised Learning
Classification and regression
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2
Unsupervised Learning
Clustering and dimensionality reduction
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3
Data Splits & Data Leakage
Train / Validation / Test Splits & Data Leakage – Practical Guide
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4
Model Evaluation
Metrics, cross-validation, confusion matrix
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5
Feature Engineering
Selection, extraction, transformation
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6
Ensemble Methods
Random Forest, XGBoost, Stacking
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