Machine Learning with Python Cookbook

• Verfasst von: Gallatin, Kyle [VerfasserIn]
• Titel: Machine Learning with Python Cookbook
• Titelzusatz: practical solutions from preprocessing to deep learning
• Mitwirkende: Albon, Chris [MitwirkendeR]
• Verf.angabe: Kyle Gallatin & Chris Albon
• Ausgabe: Second edition.
• Verlagsort: Sebastopol
• Verlag: O'Reilly Media, Incorporated
• Jahr: 2023
• Umfang: 1 online resource (416 p.)
• Fussnoten: Description based upon print version of record. - Solution
• ISBN: 978-1-0981-3569-0
• ISBN: 1-0981-3569-5
• Datenträger: Online-Ressource
• Sprache: eng
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe
• K10plus-PPN: 1859054250
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m4373573697
• Lokale URL Inst.: Zum Volltext

Abstract

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), naṽe Bayes, clustering, and tree-based models Saving, loading, and serving trained models from multiple frameworks.