Machine learning with Python cookbook: practical solutions from preprocessing to deep learning
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 f...
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| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Sebastopol
O'Reilly
July 2023
|
| Edition: | Second edition |
| Subjects: | |
| 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 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. |
| Physical Description: | xiv, 398 Seiten Illustrationen, Diagramme |
| ISBN: | 9781098135720 |
Staff View
MARC
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| 505 | 8 | |a 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 Support vector machines (SVM), naive Bayes, clustering, and tree-based models Saving and loading trained models from multiple frameworks | |
| 520 | 3 | |a 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. | |
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| contents | 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 Support vector machines (SVM), naive Bayes, clustering, and tree-based models Saving and loading trained models from multiple frameworks |
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| institution | BVB |
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| language | English |
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| spelling | Albon, Chris Verfasser (DE-588)1165271796 aut Machine learning with Python cookbook practical solutions from preprocessing to deep learning Kyle Gallatin and Chris Albon Second edition Sebastopol O'Reilly July 2023 © 2023 xiv, 398 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier 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 Support vector machines (SVM), naive Bayes, clustering, and tree-based models Saving and loading trained models from multiple frameworks 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. Machine learning Python (Computer program language) Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Python Programmiersprache (DE-588)4434275-5 gnd rswk-swf Python Programmiersprache (DE-588)4434275-5 s Maschinelles Lernen (DE-588)4193754-5 s DE-604 Gallatin, Kyle Verfasser aut |
| spellingShingle | Albon, Chris Gallatin, Kyle Machine learning with Python cookbook practical solutions from preprocessing to deep learning 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 Support vector machines (SVM), naive Bayes, clustering, and tree-based models Saving and loading trained models from multiple frameworks Machine learning Python (Computer program language) Maschinelles Lernen (DE-588)4193754-5 gnd Python Programmiersprache (DE-588)4434275-5 gnd |
| subject_GND | (DE-588)4193754-5 (DE-588)4434275-5 |
| title | Machine learning with Python cookbook practical solutions from preprocessing to deep learning |
| title_auth | Machine learning with Python cookbook practical solutions from preprocessing to deep learning |
| title_exact_search | Machine learning with Python cookbook practical solutions from preprocessing to deep learning |
| title_exact_search_txtP | Machine learning with Python cookbook practical solutions from preprocessing to deep learning |
| title_full | Machine learning with Python cookbook practical solutions from preprocessing to deep learning Kyle Gallatin and Chris Albon |
| title_fullStr | Machine learning with Python cookbook practical solutions from preprocessing to deep learning Kyle Gallatin and Chris Albon |
| title_full_unstemmed | Machine learning with Python cookbook practical solutions from preprocessing to deep learning Kyle Gallatin and Chris Albon |
| title_short | Machine learning with Python cookbook |
| title_sort | machine learning with python cookbook practical solutions from preprocessing to deep learning |
| title_sub | practical solutions from preprocessing to deep learning |
| topic | Machine learning Python (Computer program language) Maschinelles Lernen (DE-588)4193754-5 gnd Python Programmiersprache (DE-588)4434275-5 gnd |
| topic_facet | Machine learning Python (Computer program language) Maschinelles Lernen Python Programmiersprache |
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