Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning

Kyle / Albon Gallatin

380 pages, parution le 10/08/2023

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Résumé

This practical guide provides more than 200 self-contained recipes to help you solve machine learning challenges you may encounter in your work.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 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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	PAPIER
Éditeur(s)	O'Reilly
Auteur(s)	Kyle / Albon Gallatin
Parution	10/08/2023
Nb. de pages	380
EAN13	9781098135720

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Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning

Résumé

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