MARC details
| 000 -LEADER |
| fixed length control field |
01605nam a22002777i 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
| control field |
20250723155432.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
| fixed length control field |
250723s2020 ii ao||fr|||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
| International Standard Book Number |
9789389845365 |
| 040 ## - CATALOGING SOURCE |
| Original cataloging agency |
EG-GaU |
| Transcribing agency |
EG-GaU |
| Modifying agency |
EG-GaU |
| Description conventions |
rda |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
| Edition number |
23 |
| Classification number |
006..3 |
| Item number |
n.a.p. |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
| Personal name |
Nag, Avishek, |
| Relator term |
author. |
| 9 (RLIN) |
60495 |
| 245 10 - TITLE STATEMENT |
| Title |
Pragmatic machine learning with Python : |
| Remainder of title |
learn how to deploy machine learning models in production / |
| Statement of responsibility, etc. |
Avishek Nag. |
| 250 ## - EDITION STATEMENT |
| Edition statement |
1st edition. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
| Place of production, publication, distribution, manufacture |
New Delhi : |
| Name of producer, publisher, distributor, manufacturer |
BPB Publications, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2020. |
| 300 ## - PHYSICAL DESCRIPTION |
| Extent |
316 pages : |
| Other physical details |
illustrations ; |
| Dimensions |
24 cm. |
| 336 ## - CONTENT TYPE |
| Source |
rdacontent |
| Content type term |
text |
| Content type code |
txt |
| 337 ## - MEDIA TYPE |
| Source |
rdamedia |
| Media type term |
unmediated |
| Media type code |
n |
| 338 ## - CARRIER TYPE |
| Source |
rdacarrier |
| Carrier type term |
volume |
| Carrier type code |
nc |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
| Bibliography, etc. note |
Includes bibliographical references.<br/> |
| 505 #0 - FORMATTED CONTENTS NOTE |
| Formatted contents note |
1. Introduction to ML & mathematical preliminaries ‡ 2. Classification ‡ 3. Regression ‡ 4. Clustering ‡ 5. Deep learning & neural networks ‡ 6. Miscellaneous unsupervised learning ‡ 7. Text mining ‡ 8. Machine learning models in production ‡ 9. Case studies & data science storytelling.<br/> |
| 520 ## - SUMMARY, ETC. |
| Summary, etc. |
Balanced guide combining mathematical foundations with practical Python implementations. Covers supervised and unsupervised techniques, deep learning, text analytics, and strategies to deploy models in production-grade systems with PMML. ([Google Books, BPB Online, AbeBooks] :contentReference[oaicite:1]{index=1})<br/> |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
| Topical term or geographic name entry element |
Machine learning |
| General subdivision |
Study and teaching. |
| 9 (RLIN) |
60492 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
| Topical term or geographic name entry element |
Machine learning |
| General subdivision |
Implementation. |
| 9 (RLIN) |
60427 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Books |