Language:
English
繁體中文
Help
Login
Back
Switch To:
Labeled
|
MARC Mode
|
ISBD
Predictive Analytics with Microsoft ...
~
Barga, Roger.
Predictive Analytics with Microsoft Azure Machine Learning 2nd Edition
Record Type:
Language materials, printed : Monograph/item
Title/Author:
Predictive Analytics with Microsoft Azure Machine Learning 2nd Edition/ by Valentine Fontama, Roger Barga, Wee Hyong Tok.
Author:
Fontama, Valentine.
other author:
Barga, Roger.
Description:
XXIII, 291 p. 227 illus.online resource. :
Contained By:
Springer Nature eBook
Subject:
Artificial intelligence. -
Online resource:
https://doi.org/10.1007/978-1-4842-1200-4
ISBN:
9781484212004
Predictive Analytics with Microsoft Azure Machine Learning 2nd Edition
Fontama, Valentine.
Predictive Analytics with Microsoft Azure Machine Learning 2nd Edition
[electronic resource] /by Valentine Fontama, Roger Barga, Wee Hyong Tok. - 2nd ed. 2015. - XXIII, 291 p. 227 illus.online resource.
Predictive Analytics with Microsoft Azure Machine Learning, Second Edition is a practical tutorial introduction to the field of data science and machine learning, with a focus on building and deploying predictive models. The book provides a thorough overview of the Microsoft Azure Machine Learning service released for general availability on February 18th, 2015 with practical guidance for building recommenders, propensity models, and churn and predictive maintenance models. The authors use task oriented descriptions and concrete end-to-end examples to ensure that the reader can immediately begin using this new service. The book describes all aspects of the service from data ingress to applying machine learning, evaluating the models, and deploying them as web services. Learn how you can quickly build and deploy sophisticated predictive models with the new Azure Machine Learning from Microsoft. What’s New in the Second Edition? Five new chapters have been added with practical detailed coverage of: Python Integration – a new feature announced February 2015 Data preparation and feature selection Data visualization with Power BI Recommendation engines Selling your models on Azure Marketplace.
ISBN: 9781484212004
Standard No.: 10.1007/978-1-4842-1200-4doiSubjects--Topical Terms:
559380
Artificial intelligence.
LC Class. No.: Q334-342
Dewey Class. No.: 006.3
Predictive Analytics with Microsoft Azure Machine Learning 2nd Edition
LDR
:02531nam a22003735i 4500
001
965905
003
DE-He213
005
20200629154024.0
007
cr nn 008mamaa
008
201211s2015 xxu| s |||| 0|eng d
020
$a
9781484212004
$9
978-1-4842-1200-4
024
7
$a
10.1007/978-1-4842-1200-4
$2
doi
035
$a
978-1-4842-1200-4
050
4
$a
Q334-342
072
7
$a
UYQ
$2
bicssc
072
7
$a
COM004000
$2
bisacsh
072
7
$a
UYQ
$2
thema
082
0 4
$a
006.3
$2
23
100
1
$a
Fontama, Valentine.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1068173
245
1 0
$a
Predictive Analytics with Microsoft Azure Machine Learning 2nd Edition
$h
[electronic resource] /
$c
by Valentine Fontama, Roger Barga, Wee Hyong Tok.
250
$a
2nd ed. 2015.
264
1
$a
Berkeley, CA :
$b
Apress :
$b
Imprint: Apress,
$c
2015.
300
$a
XXIII, 291 p. 227 illus.
$b
online resource.
336
$a
text
$b
txt
$2
rdacontent
337
$a
computer
$b
c
$2
rdamedia
338
$a
online resource
$b
cr
$2
rdacarrier
347
$a
text file
$b
PDF
$2
rda
520
$a
Predictive Analytics with Microsoft Azure Machine Learning, Second Edition is a practical tutorial introduction to the field of data science and machine learning, with a focus on building and deploying predictive models. The book provides a thorough overview of the Microsoft Azure Machine Learning service released for general availability on February 18th, 2015 with practical guidance for building recommenders, propensity models, and churn and predictive maintenance models. The authors use task oriented descriptions and concrete end-to-end examples to ensure that the reader can immediately begin using this new service. The book describes all aspects of the service from data ingress to applying machine learning, evaluating the models, and deploying them as web services. Learn how you can quickly build and deploy sophisticated predictive models with the new Azure Machine Learning from Microsoft. What’s New in the Second Edition? Five new chapters have been added with practical detailed coverage of: Python Integration – a new feature announced February 2015 Data preparation and feature selection Data visualization with Power BI Recommendation engines Selling your models on Azure Marketplace.
650
0
$a
Artificial intelligence.
$3
559380
650
0
$a
Software engineering.
$3
562952
650
0
$a
Data mining.
$3
528622
650
1 4
$a
Artificial Intelligence.
$3
646849
650
2 4
$a
Software Engineering/Programming and Operating Systems.
$3
669780
650
2 4
$a
Data Mining and Knowledge Discovery.
$3
677765
700
1
$a
Barga, Roger.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1068172
700
1
$a
Tok, Wee Hyong.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1068174
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
776
0 8
$i
Printed edition:
$z
9781484212011
776
0 8
$i
Printed edition:
$z
9781484212028
856
4 0
$u
https://doi.org/10.1007/978-1-4842-1200-4
912
$a
ZDB-2-CWD
912
$a
ZDB-2-SXPC
950
$a
Professional and Applied Computing (SpringerNature-12059)
950
$a
Professional and Applied Computing (R0) (SpringerNature-43716)
based on 0 review(s)
Multimedia
Reviews
Add a review
and share your thoughts with other readers
Export
pickup library
Processing
...
Change password
Login