-
Introduction to Ensemble Methods
A beginner's guide to ensemble methods in machine learning, explaining how averaging and bootstrapping reduce variance and improve model performance.
-
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.
-
Decision Trees - Our First Non-Linear Classifier
Learn how decision trees work for regression, including split criteria, overfitting control, and intuitive examples.
-
Structured Perceptron & Structured SVM
Understanding how Structured Perceptron and Structured SVM learn to predict structured outputs with interdependent components.
-
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.