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Machine learning with Python cookbook : practical solutions from preprocessing to deep learning / Kyle Gallatin and Chris Albon.

By: Contributor(s): Material type: TextTextPublication details: Boston : O'Reilly Media Inc, 2023.Edition: 2nd edDescription: xiv, 398 pages : illustrations (black and white) ; 24 cmISBN:
  • 9781098135720
  • 1098135725
Subject(s): DDC classification:
  • 006.31/ G162m 23
Summary: 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.
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Holdings
Item type Current library Collection Call number Copy number Status Date due Barcode Item holds
Book Book Daffodil International University Library General Stacks Non-fiction 006.31/ G162m (Browse shelf(Opens below)) 1 Checked out 01/07/2025 030646
Book Book Daffodil International University Library General Stacks Non-fiction 006.31/ G162m (Browse shelf(Opens below)) 2 Checked out 22/11/2025 030647
Total holds: 0

Previous ed.: / Chris Albon. 2018.

Includes index.

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.

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