This book provides an introduction to Machine Learning with a hands-on approach. All code is written in Python 3 and presented using Jupyter Notebooks. Some mathematical justification is given for the methods described but the primary focus is on getting users started using the different coding methods to actually solve problems in regression, classification, and clustering. Easy recipes are given for many of the standard methods in Scikit-Learn, the most popular Python library for Machine Learning. The target audience is advanced community college or intermediate university (sophomore/junior) college students with some prior computing experience. The mathematical sections require at most basic calculus and linear algebra, though the computing recipes can be used without delving too deeply into the mathematics.