Predictive analytics with Microsoft Azure machine learning: build and deploy actionable solutions in minutes
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 s...
Gespeichert in:
| Beteiligte Personen: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
[Berkley, CA]
Apress
2015
|
| Ausgabe: | Second edition. |
| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9781484212004/?ar |
| Zusammenfassung: | 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. |
| Beschreibung: | Includes index. - Online resource; title from PDF title page (EBSCO, viewed August 31, 2015) |
| Umfang: | 1 Online-Ressource |
| ISBN: | 9781484212004 1484212002 |
Internformat
MARC
| LEADER | 00000cam a22000002 4500 | ||
|---|---|---|---|
| 001 | ZDB-30-ORH-047602481 | ||
| 003 | DE-627-1 | ||
| 005 | 20240228115928.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 191023s2015 xx |||||o 00| ||eng c | ||
| 020 | |a 9781484212004 |c electronic bk. |9 978-1-4842-1200-4 | ||
| 020 | |a 1484212002 |c electronic bk. |9 1-4842-1200-2 | ||
| 035 | |a (DE-627-1)047602481 | ||
| 035 | |a (DE-599)KEP047602481 | ||
| 035 | |a (ORHE)9781484212004 | ||
| 035 | |a (DE-627-1)047602481 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rda | ||
| 041 | |a eng | ||
| 072 | 7 | |a COM |2 bisacsh | |
| 072 | 7 | |a COM |2 bisacsh | |
| 072 | 7 | |a COM |2 bisacsh | |
| 082 | 0 | |a 005.74 |2 23 | |
| 100 | 1 | |a Barga, Roger S. |e VerfasserIn |4 aut | |
| 245 | 1 | 0 | |a Predictive analytics with Microsoft Azure machine learning |b build and deploy actionable solutions in minutes |c Roger Barga, Valentine Fontama and Wee Hyong Tok |
| 250 | |a Second edition. | ||
| 264 | 1 | |a [Berkley, CA] |b Apress |c 2015 | |
| 264 | 4 | |c ©2015 | |
| 300 | |a 1 Online-Ressource | ||
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Includes index. - Online resource; title from PDF title page (EBSCO, viewed August 31, 2015) | ||
| 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. | ||
| 630 | 2 | 0 | |a Windows Azure |
| 650 | 0 | |a Information technology |x Management | |
| 650 | 0 | |a computerwetenschappen | |
| 650 | 0 | |a computer sciences | |
| 650 | 0 | |a datamining | |
| 650 | 0 | |a data mining | |
| 650 | 0 | |a Information and Communication Technology (General) | |
| 650 | 0 | |a Informatie- en communicatietechnologie (algemeen) | |
| 650 | 4 | |a Windows Azure | |
| 650 | 4 | |a Technologie de l'information ; Gestion | |
| 650 | 4 | |a Software Engineering | |
| 650 | 4 | |a Data mining | |
| 650 | 4 | |a Program concepts ; learning to program | |
| 650 | 4 | |a COMPUTERS ; Desktop Applications ; Databases | |
| 650 | 4 | |a COMPUTERS ; Database Management ; General | |
| 650 | 4 | |a COMPUTERS ; System Administration ; Storage & Retrieval | |
| 650 | 4 | |a Information technology ; Management | |
| 700 | 1 | |a Fontama, Valentine |e VerfasserIn |4 aut | |
| 700 | 1 | |a Tok, Wee-Hyong |e VerfasserIn |4 aut | |
| 776 | 1 | |z 9781484212011 | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |z 9781484212011 |
| 966 | 4 | 0 | |l DE-91 |p ZDB-30-ORH |q TUM_PDA_ORH |u https://learning.oreilly.com/library/view/-/9781484212004/?ar |m X:ORHE |x Aggregator |z lizenzpflichtig |3 Volltext |
| 912 | |a ZDB-30-ORH | ||
| 912 | |a ZDB-30-ORH | ||
| 951 | |a BO | ||
| 912 | |a ZDB-30-ORH | ||
| 049 | |a DE-91 | ||
Datensatz im Suchindex
| DE-BY-TUM_katkey | ZDB-30-ORH-047602481 |
|---|---|
| _version_ | 1821494874550042624 |
| adam_text | |
| any_adam_object | |
| author | Barga, Roger S. Fontama, Valentine Tok, Wee-Hyong |
| author_facet | Barga, Roger S. Fontama, Valentine Tok, Wee-Hyong |
| author_role | aut aut aut |
| author_sort | Barga, Roger S. |
| author_variant | r s b rs rsb v f vf w h t wht |
| building | Verbundindex |
| bvnumber | localTUM |
| collection | ZDB-30-ORH |
| ctrlnum | (DE-627-1)047602481 (DE-599)KEP047602481 (ORHE)9781484212004 |
| dewey-full | 005.74 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 005 - Computer programming, programs, data, security |
| dewey-raw | 005.74 |
| dewey-search | 005.74 |
| dewey-sort | 15.74 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| edition | Second edition. |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>03618cam a22006612 4500</leader><controlfield tag="001">ZDB-30-ORH-047602481</controlfield><controlfield tag="003">DE-627-1</controlfield><controlfield tag="005">20240228115928.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">191023s2015 xx |||||o 00| ||eng c</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781484212004</subfield><subfield code="c">electronic bk.</subfield><subfield code="9">978-1-4842-1200-4</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">1484212002</subfield><subfield code="c">electronic bk.</subfield><subfield code="9">1-4842-1200-2</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)047602481</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KEP047602481</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ORHE)9781484212004</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)047602481</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">COM</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">COM</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">COM</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">005.74</subfield><subfield code="2">23</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Barga, Roger S.</subfield><subfield code="e">VerfasserIn</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Predictive analytics with Microsoft Azure machine learning</subfield><subfield code="b">build and deploy actionable solutions in minutes</subfield><subfield code="c">Roger Barga, Valentine Fontama and Wee Hyong Tok</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">Second edition.</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">[Berkley, CA]</subfield><subfield code="b">Apress</subfield><subfield code="c">2015</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">©2015</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Includes index. - Online resource; title from PDF title page (EBSCO, viewed August 31, 2015)</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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.</subfield></datafield><datafield tag="630" ind1="2" ind2="0"><subfield code="a">Windows Azure</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Information technology</subfield><subfield code="x">Management</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">computerwetenschappen</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">computer sciences</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">datamining</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">data mining</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Information and Communication Technology (General)</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Informatie- en communicatietechnologie (algemeen)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Windows Azure</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Technologie de l'information ; Gestion</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Software Engineering</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Data mining</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Program concepts ; learning to program</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">COMPUTERS ; Desktop Applications ; Databases</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">COMPUTERS ; Database Management ; General</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">COMPUTERS ; System Administration ; Storage & Retrieval</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Information technology ; Management</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Fontama, Valentine</subfield><subfield code="e">VerfasserIn</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tok, Wee-Hyong</subfield><subfield code="e">VerfasserIn</subfield><subfield code="4">aut</subfield></datafield><datafield tag="776" ind1="1" ind2=" "><subfield code="z">9781484212011</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="z">9781484212011</subfield></datafield><datafield tag="966" ind1="4" ind2="0"><subfield code="l">DE-91</subfield><subfield code="p">ZDB-30-ORH</subfield><subfield code="q">TUM_PDA_ORH</subfield><subfield code="u">https://learning.oreilly.com/library/view/-/9781484212004/?ar</subfield><subfield code="m">X:ORHE</subfield><subfield code="x">Aggregator</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-ORH</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-ORH</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">BO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-ORH</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-91</subfield></datafield></record></collection> |
| id | ZDB-30-ORH-047602481 |
| illustrated | Not Illustrated |
| indexdate | 2025-01-17T11:21:19Z |
| institution | BVB |
| isbn | 9781484212004 1484212002 |
| language | English |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM |
| owner_facet | DE-91 DE-BY-TUM |
| physical | 1 Online-Ressource |
| psigel | ZDB-30-ORH TUM_PDA_ORH ZDB-30-ORH |
| publishDate | 2015 |
| publishDateSearch | 2015 |
| publishDateSort | 2015 |
| publisher | Apress |
| record_format | marc |
| spelling | Barga, Roger S. VerfasserIn aut Predictive analytics with Microsoft Azure machine learning build and deploy actionable solutions in minutes Roger Barga, Valentine Fontama and Wee Hyong Tok Second edition. [Berkley, CA] Apress 2015 ©2015 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes index. - Online resource; title from PDF title page (EBSCO, viewed August 31, 2015) 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. Windows Azure Information technology Management computerwetenschappen computer sciences datamining data mining Information and Communication Technology (General) Informatie- en communicatietechnologie (algemeen) Technologie de l'information ; Gestion Software Engineering Data mining Program concepts ; learning to program COMPUTERS ; Desktop Applications ; Databases COMPUTERS ; Database Management ; General COMPUTERS ; System Administration ; Storage & Retrieval Information technology ; Management Fontama, Valentine VerfasserIn aut Tok, Wee-Hyong VerfasserIn aut 9781484212011 Erscheint auch als Druck-Ausgabe 9781484212011 |
| spellingShingle | Barga, Roger S. Fontama, Valentine Tok, Wee-Hyong Predictive analytics with Microsoft Azure machine learning build and deploy actionable solutions in minutes Windows Azure Information technology Management computerwetenschappen computer sciences datamining data mining Information and Communication Technology (General) Informatie- en communicatietechnologie (algemeen) Technologie de l'information ; Gestion Software Engineering Data mining Program concepts ; learning to program COMPUTERS ; Desktop Applications ; Databases COMPUTERS ; Database Management ; General COMPUTERS ; System Administration ; Storage & Retrieval Information technology ; Management |
| title | Predictive analytics with Microsoft Azure machine learning build and deploy actionable solutions in minutes |
| title_auth | Predictive analytics with Microsoft Azure machine learning build and deploy actionable solutions in minutes |
| title_exact_search | Predictive analytics with Microsoft Azure machine learning build and deploy actionable solutions in minutes |
| title_full | Predictive analytics with Microsoft Azure machine learning build and deploy actionable solutions in minutes Roger Barga, Valentine Fontama and Wee Hyong Tok |
| title_fullStr | Predictive analytics with Microsoft Azure machine learning build and deploy actionable solutions in minutes Roger Barga, Valentine Fontama and Wee Hyong Tok |
| title_full_unstemmed | Predictive analytics with Microsoft Azure machine learning build and deploy actionable solutions in minutes Roger Barga, Valentine Fontama and Wee Hyong Tok |
| title_short | Predictive analytics with Microsoft Azure machine learning |
| title_sort | predictive analytics with microsoft azure machine learning build and deploy actionable solutions in minutes |
| title_sub | build and deploy actionable solutions in minutes |
| topic | Windows Azure Information technology Management computerwetenschappen computer sciences datamining data mining Information and Communication Technology (General) Informatie- en communicatietechnologie (algemeen) Technologie de l'information ; Gestion Software Engineering Data mining Program concepts ; learning to program COMPUTERS ; Desktop Applications ; Databases COMPUTERS ; Database Management ; General COMPUTERS ; System Administration ; Storage & Retrieval Information technology ; Management |
| topic_facet | Windows Azure Information technology Management computerwetenschappen computer sciences datamining data mining Information and Communication Technology (General) Informatie- en communicatietechnologie (algemeen) Technologie de l'information ; Gestion Software Engineering Data mining Program concepts ; learning to program COMPUTERS ; Desktop Applications ; Databases COMPUTERS ; Database Management ; General COMPUTERS ; System Administration ; Storage & Retrieval Information technology ; Management |
| work_keys_str_mv | AT bargarogers predictiveanalyticswithmicrosoftazuremachinelearningbuildanddeployactionablesolutionsinminutes AT fontamavalentine predictiveanalyticswithmicrosoftazuremachinelearningbuildanddeployactionablesolutionsinminutes AT tokweehyong predictiveanalyticswithmicrosoftazuremachinelearningbuildanddeployactionablesolutionsinminutes |