DATA
“I was worried that I
would never be able
to find a cookbook
that concisely covers
ANN, hyperplanes, and
random forest feature
selection, but then this
text came into my life.”
—Vicki Boykis
Machine Learning Engineer, Duo
Machine Learning with Python Cookbook
Twitter: @oreillymedia
linkedin.com/company/oreilly-media
youtube.com/oreillymedia
This practical guide provides more than 200 self-contained
recipes to help you solve machine learning challenges you
may encounter in your work. If you’re comfortable with
Python and its libraries, including pandas and scikit-learn,
you’ll be able to address specific problems, from loading
data to training models and leveraging neural networks.
Each recipe in this updated edition includes code that you
can copy, paste, and run with a toy dataset to ensure that it
works. From there, you can adapt these recipes according to
your use case or application. Recipes include a discussion that
explains the solution and provides meaningful context.
Go beyond theory and concepts by learning the nuts and
bolts you need to construct working machine learning
applications. You’ll find recipes for:
• Vectors, matrices, and arrays
• Working with data from CSV, JSON, SQL, databases,
cloud storage, and other sources
• Handling numerical and categorical data, text, images,
and dates and times
• Dimensionality reduction using feature extraction
or feature selection
• Model evaluation and selection
• Linear and logical regression, trees and forests,
and k-nearest neighbors
• Supporting vector machines (SVM), naïve Bayes,
clustering, and tree-based models
• Saving, loading, and serving trained models from
multiple frameworks
Kyle Gallatin is a software
engineer on the machine learning
platform team at Etsy. He has
years of experience as a data
analyst, data scientist, and
machine learning engineer.
Chris Albon is the director
of machine learning at the
Wikimedia Foundation, the
nonprofit that hosts Wikipedia.
US $79.99 CAN $99.99
ISBN: 9781098135720
SECOND
EDITION
