000			03388nam a22003617a 4500
003			OSt
005			20260924111234.0
008			260924b |||||||| |||| 00| 0 eng d
020			_a9781098135720 [paperback]
040			_aUniversity of Cebu-Banilad _cUniversity of Cebu-Banilad
100			_aGallatin, Kyle, _eauthor.
245			_aMachine learning with python cookbook : _bpractical solutions from preprocessing to deep learning / _cby Kyle Gallatin and Chris Albon.
250			_aSecond edition.
260			_aGravenstein Highway North, Sebastopol, CA : _bO'Reilly Media, Inc., _cc2023.
300			_axiv, 398 pages : _billustrations (black and white) ; _c23 cm.
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
504			_aIncludes index.
505			_aContents: 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.
520			_a"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
521			_aAdult
541			_aPurchased _xOrtega, Eric _yCollege of Computer Studies _zComputer Science
546			_aText in English
650			_aMachine learning.
650			_aPython (Computer program language).
650			_aData processing _xHandbooks, manuals, etc.
650			_aComputer programs _xHandbooks, manuals, etc.
700			_aAlbon, Chris, _eauthor.
942			_2ddc _cBK
998			_cJanna [new] _d09/24/2026
999			_c15695 _d15695