Differential privacy has emerged as one of the de-facto standards for measuring privacy risk when performing computations on sensitive data and disseminating the results. Algorithms that guarantee differential privacy are randomized, which causes a loss in performance, or utility. Managing the privacy-utility tradeoff becomes easier with more data. Many machine learning algorithms can be made differentially private through the judicious introduction of randomization, usually through noise, within the computation. In this tutorial we will describe the basic framework of differential privacy, key mechanisms for guaranteeing privacy, and how to find differentially private approximations to several contemporary machine learning tools: convex optimization, Bayesian methods, and deep learning.