A hands-on tutorial introducing differential privacy through practical implementations of Laplace and Gaussian mechanisms, DP statistics (mean and histogram), and differentially private logistic regression. Demonstrates the fundamental privacy-utility tradeoff where increasing epsilon improves model accuracy but weakens privacy guarantees.