With the inflow of large amounts of data taken by the next generation of telescopes, Machine Learning (ML) techniques are becoming more and more important for astronomers. The goal of this workshop is to open the door to various ML techniques for astronomers. The workshop is organized in three tutorials of different difficulty levels to reach a broad level of expertise. Participants learn using Jupyter Notebooks how to use machine learning to classify spheroid and disk galaxies, reduce Spitzer/IRAC data, and de-noise LIGO data to detect gravitational waves signatures. The workshop is organized by Caltech/IPAC in collaboration with the University of Illinois, Urbana-Champaign, and run on AWS servers with Amazon credits.