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Decision Trees for Classification
Explains what makes a good split, how impurity is quantified using Gini, Entropy, and misclassification error, and why trees are both powerful and interpretable.
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Decision Trees - Our First Non-Linear Classifier
Learn how decision trees work for regression, including split criteria, overfitting control, and intuitive examples.
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Structured Perceptron & Structured SVM
Understanding how Structured Perceptron and Structured SVM learn to predict structured outputs with interdependent components.
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Structured Prediction and Multiclass SVM
An in-depth yet intuitive walkthrough of structured prediction, covering sequence labeling, feature engineering, and scoring methods for complex outputs.
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Multiclass Classification with SVM
Learn how Support Vector Machines extend to multiclass classification with an intuitive breakdown of margin concepts, loss derivation, and the multiclass hinge loss formulation.