Machine learning is the branch of computer science concerned with the development of algorithms which can be trained by previously-seen data in order to make predictions about future data. It has become an important aspect of work in a variety of applications: from optimization of web searches, to financial forecasts, to studies of the nature of the Universe. This tutorial will explore machine learning with a hands-on introduction to the scikit-learn package. Beginning from the broad categories of *supervised* and *unsupervised* learning problems, we will dive into the fundamental areas of *classification*, *regression*, *clustering*, and *dimensionality reduction*. In each section, we will introduce aspects of the Scikit-learn API and explore practical examples of some of the most popular and useful methods from the machine learning literature. The strengths of scikit-learn lie in its uniform and well-document interface, and its efficient implementations of a large number of the most important machine learning algorithms. Those present at this tutorial will gain a basic practical background in machine learning and the use of scikit-learn, and will be well poised to begin applying these tools in many areas, whether for work, for research, for Kaggle-style competitions, or for their own pet projects.