Getting Started in Machine Learning:

ISBN-13: 978-0-9966860-6-8

ISBN-10: 0-9966860-6-1

BISAC: Computers / Mathematical & Statistical Software

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.