| 000 -LEADER |
|---|
| fixed length control field |
03388nam a22003617a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20260924111234.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
260924b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781098135720 [paperback] |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
University of Cebu-Banilad |
| Transcribing agency |
University of Cebu-Banilad |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Gallatin, Kyle, |
| Relator term |
author. |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Machine learning with python cookbook : |
| Remainder of title |
practical solutions from preprocessing to deep learning / |
| Statement of responsibility, etc |
by Kyle Gallatin and Chris Albon. |
| 250 ## - EDITION STATEMENT |
|---|
| Edition statement |
Second edition. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
Gravenstein Highway North, Sebastopol, CA : |
| Name of publisher, distributor, etc |
O'Reilly Media, Inc., |
| Date of publication, distribution, etc |
c2023. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xiv, 398 pages : |
| Other physical details |
illustrations (black and white) ; |
| Dimensions |
23 cm. |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
|---|
| Source |
rdamedia |
| Media type term |
unmediated |
| 338 ## - CARRIER TYPE |
|---|
| Source |
rdacarrier |
| Carrier type |
volume |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Includes index. |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
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. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
"This practical guide provides more than 200 self-contained recipes to help you solve machine learning challenges you may encounter in your work.<br/><br/>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.<br/><br/>From there, you can adapt these recipes according to your use case or application.<br/><br/>Recipes include a discussion that explains the solution and provides meaningful context.<br/><br/>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 ## - TARGET AUDIENCE NOTE |
|---|
| Target audience note |
Adult |
| 541 ## - IMMEDIATE SOURCE OF ACQUISITION NOTE |
|---|
| Source of acquisition |
Purchased |
| Deans/Chairperson |
Ortega, Eric |
| Department |
College of Computer Studies |
| Subject Category |
Computer Science |
| 546 ## - LANGUAGE NOTE |
|---|
| Language note |
Text in English |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Machine learning. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Python (Computer program language). |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Data processing |
| General subdivision |
Handbooks, manuals, etc. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Computer programs |
| General subdivision |
Handbooks, manuals, etc. |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Albon, Chris, |
| Relator term |
author. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Type of record |
Book |
| 998 ## - LOCAL CONTROL INFORMATION (RLIN) |
|---|
| Encoded by |
Janna [new] |
| Date encoded |
09/24/2026 |
