Machine Learning with Python Cookbook

Kyle Gallatin, Chris Albon

ISBN 10: 1098135725 / ISBN 13: 9781098135720

Editore: O'Reilly Media, US, 2023

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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 all the way 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 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 arraysWorking with data from CSV, JSON, SQL, databases, cloud storage, and other sourcesHandling numerical and categorical data, text, images, and dates and timesDimensionality reduction using feature extraction or feature selectionModel evaluation and selectionLinear and logical regression, trees and forests, and k-nearest neighborsSupport vector machines (SVM), naive Bayes, clustering, and tree-based modelsSaving and loading trained models from multiple frameworks.

Codice articolo LU-9781098135720

Dati bibliografici

• Titolo: Machine Learning with Python Cookbook
• Editore: O'Reilly Media, US
• Data di pubblicazione: 2023
• Lingua: Inglese
• ISBN 10: 1098135725
• ISBN 13: 9781098135720
• Rilegatura: Paperback
• Edizione: 2nd.
• Condizione: New
• Dimensioni: 17,78 L x 2,16 A x 23,34 P centimetri 17,78 centimetri di larghezza per 2,16 centimetri di altezza per 23,34 centimetri di profondità

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Riassunto

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