This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you’re comfortable with Python and its libraries, including Pandas and Scikit-learn, author Chris Albon shows how to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality reduction and many other topics.

Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your application. Recipes also include a discussion that explains the solution and provides meaningful context.

Author: Chris Albon
Publisher: O'Reilly
Date: April 2018
Pages: 366
ISBN: 978-1491989388
Print: 1491989386
Kindle: B07BC3LFKT
Audience: Python developers
Level: Intermediate
Category: Artificial Intelligence and Python

• Vectors, matrices, and arrays
• 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
• Support vector machines (SVM), naïve Bayes, clustering, and neural networks
• Saving and loading trained models

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