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Titel
Machine learning with Python cookbook : practical solutions from preprocessing to deep learning / Kyle Gallatin and Chris Albon
VerfasserGallatin, Kyle In Wikipedia suchen nach Kyle Gallatin ; Albon, Chris In der Gemeinsamen Normdatei der DNB nachschlagen In Wikipedia suchen nach Chris Albon
ErschienenBeijing : O'Reilly, July 2023 ; © 2023
Ausgabe
Second edition
Umfangxiv, 398 Seiten : Illustrationen, Diagramme
SchlagwörterMachine learning In Wikipedia suchen nach Machine learning / Python (Computer program language) In Wikipedia suchen nach Python (Computer program language) / bisacsh COMPUTERS Data Science Machine Learning In Wikipedia suchen nach bisacsh COMPUTERS Data Science Machine Learning / bisacsh COMPUTERS Data Science Neural Networks In Wikipedia suchen nach bisacsh COMPUTERS Data Science Neural Networks / bisacsh COMPUTERS Programming General In Wikipedia suchen nach bisacsh COMPUTERS Programming General / bisacsh COMPUTERS Languages Python In Wikipedia suchen nach bisacsh COMPUTERS Languages Python / bisacsh COMPUTERS Artificial Intelligence General In Wikipedia suchen nach bisacsh COMPUTERS Artificial Intelligence General / Python <Programmiersprache> In Wikipedia suchen nach Python Programmiersprache / Maschinelles Lernen In Wikipedia suchen nach Maschinelles Lernen
ISBN978-1-098-13572-0
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Download Machine learning with Python cookbook [1,10 mb]
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Zusammenfassung

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.