Gallatin, Kyle,
Machine learning with python cookbook : practical solutions from preprocessing to deep learning / by Kyle Gallatin and Chris Albon. - Second edition. - Gravenstein Highway North, Sebastopol, CA : O'Reilly Media, Inc., c2023. - xiv, 398 pages : illustrations (black and white) ; 23 cm.
Includes index.
Contents: 1. Working with vectors, matrices, and arrays in NumPy -- 2. Loading data -- 3. Data wrangling -- 4. Handling numerical data -- 5. Handling categorical data -- 6. Handling text -- 7. Handling dates and times -- 8. Handling images -- 9. Dimensionality reduction using feature extraction -- 10 Dimensionality using feature selection -- 11. Model evaluation -- 12. Model selection -- 13. Linear regression -- 14. Trees and forests -- 15. K-nearest neighbors -- 16. Logistic regression -- 17. Support vector machines -- 18. Naive bayes -- 19. Clustering -- 20. Tensors with PyTorch -- 21. Neural networks -- 22. Neural networks for unstructured data -- 23. Savings, loasding, and serving trained models.
"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 arraysWorking with data from CSV, JSON, SQL, databases, cloud storage, and other sourcesHandling numerical and categorical data, text, images, and dates and timesDimensionality reduction using feature extraction or feature selectionModel evaluation and selectionLinear and logical regression, trees and forests, and k-nearest neighborsSupport vector machines (SVM), naive Bayes, clustering, and tree-based modelsSaving and loading trained models from multiple frameworks." --Provided by the publisher
Adult
Text in English
9781098135720 [paperback]
Machine learning.
Python (Computer program language).
Data processing--Handbooks, manuals, etc.
Computer programs--Handbooks, manuals, etc.
Machine learning with python cookbook : practical solutions from preprocessing to deep learning / by Kyle Gallatin and Chris Albon. - Second edition. - Gravenstein Highway North, Sebastopol, CA : O'Reilly Media, Inc., c2023. - xiv, 398 pages : illustrations (black and white) ; 23 cm.
Includes index.
Contents: 1. Working with vectors, matrices, and arrays in NumPy -- 2. Loading data -- 3. Data wrangling -- 4. Handling numerical data -- 5. Handling categorical data -- 6. Handling text -- 7. Handling dates and times -- 8. Handling images -- 9. Dimensionality reduction using feature extraction -- 10 Dimensionality using feature selection -- 11. Model evaluation -- 12. Model selection -- 13. Linear regression -- 14. Trees and forests -- 15. K-nearest neighbors -- 16. Logistic regression -- 17. Support vector machines -- 18. Naive bayes -- 19. Clustering -- 20. Tensors with PyTorch -- 21. Neural networks -- 22. Neural networks for unstructured data -- 23. Savings, loasding, and serving trained models.
"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 arraysWorking with data from CSV, JSON, SQL, databases, cloud storage, and other sourcesHandling numerical and categorical data, text, images, and dates and timesDimensionality reduction using feature extraction or feature selectionModel evaluation and selectionLinear and logical regression, trees and forests, and k-nearest neighborsSupport vector machines (SVM), naive Bayes, clustering, and tree-based modelsSaving and loading trained models from multiple frameworks." --Provided by the publisher
Adult
Text in English
9781098135720 [paperback]
Machine learning.
Python (Computer program language).
Data processing--Handbooks, manuals, etc.
Computer programs--Handbooks, manuals, etc.