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Machine Learning with Python Cookbook : Practical Solutions from Preprocessing to Deep Learning by Kyle Gallatin and Chris Albon (2023, Trade Paperback)
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Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning (Paperback or Softback). Your Privacy. ISBN: 9781098135720. Condition Guide. Publication Date: 9/5/2023. Item Availability.
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About this product
Product Identifiers
PublisherO'reilly Media, Incorporated
ISBN-101098135725
ISBN-139781098135720
eBay Product ID (ePID)22060891058
Product Key Features
Number of Pages380 Pages
LanguageEnglish
Publication NameMachine Learning with Python Cookbook : Practical Solutions from Preprocessing to Deep Learning
SubjectProgramming / General, Intelligence (Ai) & Semantics, Neural Networks, Programming Languages / Python
Publication Year2023
TypeTextbook
Subject AreaComputers
AuthorKyle Gallatin, Chris Albon
FormatTrade Paperback
Dimensions
Item Height0.9 in
Item Weight25.7 Oz
Item Length9.2 in
Item Width7.5 in
Additional Product Features
Edition Number2
LCCN2023-302490
Dewey Edition23
IllustratedYes
Dewey Decimal006.31
SynopsisThis 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, 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), naäve Bayes, clustering, and tree-based models Saving, loading, and serving trained models from multiple frameworks
LC Classification NumberQ325.5.A425 2023
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