語系:
繁體中文
English
說明(常見問題)
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Predictive Analytics with Microsoft ...
~
Barga, Roger.
Predictive Analytics with Microsoft Azure Machine Learning 2nd Edition
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Predictive Analytics with Microsoft Azure Machine Learning 2nd Edition/ by Valentine Fontama, Roger Barga, Wee Hyong Tok.
作者:
Fontama, Valentine.
其他作者:
Barga, Roger.
面頁冊數:
XXIII, 291 p. 227 illus.online resource. :
Contained By:
Springer Nature eBook
標題:
Artificial intelligence. -
電子資源:
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)
筆 0 讀者評論
多媒體
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼[密碼必須為2種組合(英文和數字)及長度為10碼以上]
登入