Machine learning python cookbook von albon chris (12 Ergebnisse)
Sprache: Englisch
Verlag: O'Reilly Media, 2018
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Sprache: Englisch
Verlag: O'Reilly Media, 2018
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Sprache: Englisch
Verlag: O'Reilly Media, 2018
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Sprache: Englisch
Verlag: Oâ²Reilly, 2018
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Paperback. Zustand: Brand New. 2nd edition. 380 pages. 9.19x7.00x0.85 inches. In Stock.
Sprache: Englisch
Verlag: O'Reilly Media, 2018
- Softcover
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Zustand: Gut. 349 S. - Wir versenden aus unserem deutschen Lager heraus in plastikfreien oder wiederverwendeten Polstertaschen. Sprache: Englisch Gewicht in Gramm: 666 Taschenbuch, Maße: 17.15 cm x 1.91 cm x 22.86 cm.
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Taschenbuch. Zustand: Neu. Neuware -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 dat…a 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 frameworksLibri GmbH, Europaallee 1, 36244 Bad Hersfeld Englisch.
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Taschenbuch. Zustand: Neu. Neuware - 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 da…ta 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.
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Taschenbuch. Zustand: Neu. Neuware -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 dat…a 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 Englisch.
