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we kick off with ensemble learning - why combining models beats a single model. random forest, xgboost, sampling strategies, and how ensembles reduce overfitting by design.

then into neural networks: how layers of linear + nonlinear transforms learn patterns from raw data. we trace the evolution: cnns, rnns, transformers + attention, and how that leap unlocked llms.

training + evaluation essentials: loss functions, gradient descent, learning rates, proper train/val/test splits, and k-fold cross-validation. plus the metrics you actually need: precision, recall, f1, auc, confusion matrices.