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Ten detailed real-world case studies showing XGBoost applied to credit risk assessment, fraud detection, healthcare diagnosis, energy forecasting, web search ranking, and more. Each case study covers why XGBoost was chosen over alternatives, what features were engineered, and what results were achieved. Moves the algorithm from abstract to concrete.

A practitioner with real industry experience compares Random Forest and XGBoost across multiple high-stakes projects. Goes beyond accuracy numbers to cover scalability, interpretability trade-offs, training time, and what actually breaks in production. Gives specific guidance on when to choose each, which is the question students always have after learning both.

A single, well-illustrated article covering three Week 4 algorithms with intuition-first diagrams. Decision trees explained through flowchart analogy and impurity measures. Random Forests through ensemble voting and bagging. SVMs through hyperplanes, margins, and the kernel trick. Designed to build conceptual clarity before diving into implementation.

A clean step-by-step walkthrough of how Random Forests work, starting from a single decision tree, through bootstrap sampling and random feature selection, to the final voting ensemble. Includes live decision boundary plots showing the difference between a single tree's jagged boundary and the forest's smoother generalization. One of the clearest explanations of why the ensemble outperforms any individual component.

An end-to-end credit risk case study applying all three Week 4 tree-based algorithms in sequence on the same dataset. Compares AUC scores across algorithms, tests five different learning rates over 200 boosting rounds for XGBoost, and gives honest reflection on what hyperparameter tuning actually changes. One of the clearest illustrations of why you progress from simple to complex models.