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), naäve Bayes, clustering, and tree-based models
- Saving, loading, and serving trained models from multiple frameworks
About this Ebook
File formats
This ebook is available in:
PDF (drm free)
EPUB (drm free)
Publisher
O'Reilly Media
Published
Jan 29 2026
Title
Machine Learning with Python Cookbook
Author
Kyle Gallatin ; Chris Albon
Edition
2
Imprint
O'Reilly Media
Language
English
Number of Pages
503
