SKOLKOVO School of Management

Introduction to machine learning with Python : (Record no. 4781)

000 -LEADER
fixed length control field 02421cam a22003977i 4500
001 - CONTROL NUMBER
control field 19777557
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240321112206.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 170710t20162017caua 001 0 eng d
010 ## - LIBRARY OF CONGRESS CONTROL NUMBER
LC control number 2017394288
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781449369415
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 1449369413
035 ## - SYSTEM CONTROL NUMBER
System control number (OCoLC)ocn895728667
040 ## - CATALOGING SOURCE
Original cataloging agency BTCTA
Language of cataloging eng
Transcribing agency BTCTA
Description conventions rda
Modifying agency YDXCP
-- BDX
-- OCLCQ
-- JBL
-- APL
-- TEF
-- OCLCF
-- AHS
-- CHVBK
-- OCLCO
-- MVP
-- FIE
-- I8M
-- CDN
-- OCLCQ
-- DLC
042 ## - AUTHENTICATION CODE
Authentication code lccopycat
050 00 - LIBRARY OF CONGRESS CALL NUMBER
Classification number QA76.73.P98
Item number M85 2016
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 005.133
Edition number 23
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Müller, Andreas C.,
Relator term author.
245 10 - TITLE STATEMENT
Title Introduction to machine learning with Python :
Remainder of title a guide for data scientists /
Statement of responsibility, etc Andreas C. Müller and Sarah Guido.
246 30 - VARYING FORM OF TITLE
Title proper/short title Machine learning with Python
250 ## - EDITION STATEMENT
Edition statement First edition.
300 ## - PHYSICAL DESCRIPTION
Extent xii, 376 pages :
Other physical details illustrations ;
Dimensions 24 cm
500 ## - GENERAL NOTE
General note Includes index.
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Introduction -- Supervised learning -- Unsupervised learning and preprocessing -- Representing data and engineering features -- Model evaluation and improvement -- Algorithm chains and pipelines -- Working with text data -- Wrapping up.
520 ## - SUMMARY, ETC.
Summary, etc Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book. --
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Python (Computer program language)
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Programming languages (Electronic computers)
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Data mining.
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Data mining.
Source of heading or term fast
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Programming languages (Electronic computers)
Source of heading or term fast
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Python (Computer program language)
Source of heading or term fast
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Maschinelles Lernen
Source of heading or term gnd
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Guido, Sarah,
Relator term author.
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 Permanent Location Current Location Shelving location Date acquired Total Checkouts Full call number Barcode Date last seen Date checked out Price effective from Koha item type
          SKOLKOVO Library SKOLKOVO Library Shelves 2024-04-01   QA76.73.P98 M85 2016   2024-04-01   2024-04-01 E-Book
          Skoltech library Skoltech library Shelves 2024-03-21 1 QA76.73.P98 M85 2016 2000007915 2024-09-12 2024-05-28 2024-03-21 Book