Asia Pacific University Library catalogue


Feature engineering for machine learning : (Record no. 382309)

000 -LEADER
fixed length control field 01529cam a22003257i 4500
001 - CONTROL NUMBER
control field 20640679
003 - CONTROL NUMBER IDENTIFIER
control field APU
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20190226074256.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 190226t20182018cc a b 001 0 eng d
010 ## - LIBRARY OF CONGRESS CONTROL NUMBER
LC control number 2018302039
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781491953242 (pbk.)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 1491953241 (pbk.)
040 ## - CATALOGING SOURCE
Original cataloging agency BTCTA
Language of cataloging eng
Transcribing agency BTCTA
Modifying agency WAN
042 ## - AUTHENTICATION CODE
Authentication code lccopycat
050 00 - LIBRARY OF CONGRESS CALL NUMBER
Classification number Q325.5
Item number .Z44 2018
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.31
Edition number 23
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Zheng, Alice.
9 (RLIN) 42364
245 10 - TITLE STATEMENT
Title Feature engineering for machine learning :
Remainder of title principles and techniques for data scientists /
Statement of responsibility, etc Alice Zheng and Amanda Casari.
250 ## - EDITION STATEMENT
Edition statement 1st ed.
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc Beijing :
-- Boston
Name of publisher, distributor, etc O'Reilly,
Date of publication, distribution, etc 2018.
300 ## - PHYSICAL DESCRIPTION
Extent xiii, 200 p.:
Other physical details ill. ;
Dimensions 24 cm
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc Includes bibliographical references and index.
520 ## - SUMMARY, ETC.
Summary, etc Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, youll learn techniques for extracting and transforming featuresthe numeric representations of raw datainto formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering.--
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine learning.
9 (RLIN) 42365
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Data mining.
9 (RLIN) 42366
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Casari, Amanda.
9 (RLIN) 42367
906 ## - LOCAL DATA ELEMENT F, LDF (RLIN)
a 7
b cbc
c copycat
d 2
e ncip
f 20
g y-gencatlg
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme
Koha item type Book
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Home library Current library Shelving location Date acquired Source of acquisition Invoice number Cost, normal purchase price Total Checkouts Total Renewals Full call number Barcode Date last seen Date checked out Copy number Cost, replacement price Price effective from Koha item type PO number
Not Withdrawn Available   Not Damaged Available for loan Book APU Library APU Library Open Shelf 26/02/2019 EMO I-027511 176.40 2 1 Q325.5 .Z44 2018 c.1 00012388 02/03/2020 24/02/2020 1 176.40 26/02/2019 Staff Circulation U-2018/12/0889
Not Withdrawn Available   Not Damaged Available for loan Book APU Library APU Library Open Shelf 26/02/2019 EMO I-027511 176.40 18 24 Q325.5 .Z44 2018 c.2 00012389 22/06/2024 18/06/2024 2 176.40 26/02/2019 General Circulation U-2018/12/0889