Overview
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), naƤve Bayes, clustering, and tree-based models
- Saving, loading, and serving trained models from multiple frameworks
This book title, Machine Learning with Python Cookbook (Practical Solutions from Preprocessing to Deep Learning) - 9781098135720, ISBN: 9781098135720, by Kyle Gallatin, Chris Albon, published by O'Reilly Media (November 28, 2023) is available in paperback. Our minimum order quantity is 25 copies. All standard bulk book orders ship FREE in the continental USA and delivered in 4-10 business days.
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