Chris Albon - Machine Learning with Python Cookbook

by Chris Albon

Copyright 2018 Chris Albon. All rights reserved.

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• April 2018: First Edition

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Over the last few years machine learning has become embedded in a widevariety of day-to-day business, nonprofit, and government operations.As the popularity of machine learning increased, a cottage industry ofhigh-quality literature that taught applied machine learning topractitioners developed. This literature has been highly successful intraining an entire generation of data scientists and machine learningengineers. This literature also approached the topic of machine learningfrom the perspective of providing a learning resource to teach anindividual what machine learning is and how it works. However, whilefruitful, this approach left out a different perspective on the topic:the nuts and bolts of doing machine learning day to day. That is themotivation of this booknot as a tome of machine learning knowledgefor the student but as a wrench for the professional, to sit withdog-eared pages on desks ready to solve the practical day-to-dayproblems of a machine learning practitioner.

More specifically, the book takes a task-based approach to machinelearning, with almost 200 self-contained solutions (you can copy and paste the code and itll run) for the most common tasks a datascientist or machine learning engineer building a model will runinto.

The ultimate goal is for the book to be a reference for people buildingreal machine learning systems. For example, imagine a reader has a JSONfile containing 1,000 categorical and numerical features with missing data and categorical target vectors with imbalanced classes, and wants an interpretable model. The motivation for this book is to provide recipes to help the reader learn processes such as:

The goal is for the reader to be able to:

1. Copy/paste the code and gain confidence that it actually works withthe included toy dataset.

2. Read the discussion to gain an understanding of the theory behind thetechnique the code is executing and learn which parameters are important to consider.

3. Insert/combine/adapt the code from the recipes to construct the actual application.