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.

<ASIN:1491989386>

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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