東華大學圖書館 |

Machine Learning with Microsoft technologies = selecting the right architecture and tools for your project /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine Learning with Microsoft technologies/ by Leila Etaati.
其他題名:	selecting the right architecture and tools for your project /
作者:	Etaati, Leila.
出版者:	Berkeley, CA :Apress : : 2019.,
面頁冊數:	xv, 365 p. :ill., digital ;24 cm.
內容註:	Part I: Getting Started -- Chapter 1: Introduction to Machine Learning -- Chapter 2: Introduction to R -- Chapter 3: Introduction to Python -- Chapter 4: R Visualization in Power BI -- Part II: Machine Learning in R and Power BI -- Chapter 5: Business Understanding -- Chapter 6: Data Wrangling for Predictive Analysis -- Chapter 7: Predictive Analysis in Power Query with R -- Chapter 8: Descriptive Analysis in Power Query with R -- Part III: Machine Learning SQL Server -- Chapter 9: Using R with SQL Server 2016 and 2017 -- Chapter 10: Azure Databricks -- Part IV: Machine Learning in Azure -- Chapter 11: R in Azure Data Lake -- Chapter 12: Azure Machine Learning Studio -- Chapter 13: Machine Learning in Azure Stream Analytics -- Chapter 14: Azure Machine Learning (ML) Workbench -- Chapter 15: Machine Learning on HDInsight -- Chapter 16: Data Science Virtual Machine and AI Framework -- Chapter 17: Deep Learning Tools with Cognitive Toolkit (CNTK) -- Part V: Data Science Virtual Machine -- Chapter 18: Cognitive Service Toolkit -- Chapter 19: Bot Framework -- Chapter 20: Overview on Microsoft Machine Learning Tools.
Contained By:	Springer eBooks
標題:	Machine learning. -
電子資源:	https://doi.org/10.1007/978-1-4842-3658-1
ISBN:	9781484236581

Machine Learning with Microsoft technologies = selecting the right architecture and tools for your project /
Etaati, Leila.

Machine Learning with Microsoft technologiesselecting the right architecture and tools for your project /[electronic resource] :by Leila Etaati. - Berkeley, CA :Apress :2019. - xv, 365 p. :ill., digital ;24 cm.

Part I: Getting Started -- Chapter 1: Introduction to Machine Learning -- Chapter 2: Introduction to R -- Chapter 3: Introduction to Python -- Chapter 4: R Visualization in Power BI -- Part II: Machine Learning in R and Power BI -- Chapter 5: Business Understanding -- Chapter 6: Data Wrangling for Predictive Analysis -- Chapter 7: Predictive Analysis in Power Query with R -- Chapter 8: Descriptive Analysis in Power Query with R -- Part III: Machine Learning SQL Server -- Chapter 9: Using R with SQL Server 2016 and 2017 -- Chapter 10: Azure Databricks -- Part IV: Machine Learning in Azure -- Chapter 11: R in Azure Data Lake -- Chapter 12: Azure Machine Learning Studio -- Chapter 13: Machine Learning in Azure Stream Analytics -- Chapter 14: Azure Machine Learning (ML) Workbench -- Chapter 15: Machine Learning on HDInsight -- Chapter 16: Data Science Virtual Machine and AI Framework -- Chapter 17: Deep Learning Tools with Cognitive Toolkit (CNTK) -- Part V: Data Science Virtual Machine -- Chapter 18: Cognitive Service Toolkit -- Chapter 19: Bot Framework -- Chapter 20: Overview on Microsoft Machine Learning Tools.

Know how to do machine learning with Microsoft technologies. This book teaches you to do predictive, descriptive, and prescriptive analyses with Microsoft Power BI, Azure Data Lake, SQL Server, Stream Analytics, Azure Databricks, HD Insight, and more. The ability to analyze massive amounts of real-time data and predict future behavior of an organization is critical to its long-term success. Data science, and more specifically machine learning (ML), is today's game changer and should be a key building block in every company's strategy. Managing a machine learning process from business understanding, data acquisition and cleaning, modeling, and deployment in each tool is a valuable skill set. Machine Learning with Microsoft Technologies is a demo-driven book that explains how to do machine learning with Microsoft technologies. You will gain valuable insight into designing the best architecture for development, sharing, and deploying a machine learning solution. This book simplifies the process of choosing the right architecture and tools for doing machine learning based on your specific infrastructure needs and requirements. Detailed content is provided on the main algorithms for supervised and unsupervised machine learning and examples show ML practices using both R and Python languages, the main languages inside Microsoft technologies. What You'll Learn: Choose the right Microsoft product for your machine learning solution Create and manage Microsoft's tool environments for development, testing, and production of a machine learning project Implement and deploy supervised and unsupervised learning in Microsoft products Set up Microsoft Power BI, Azure Data Lake, SQL Server, Stream Analytics, Azure Databricks, and HD Insight to perform machine learning Set up a data science virtual machine and test-drive installed tools, such as Azure ML Workbench, Azure ML Server Developer, Anaconda Python, Jupyter Notebook, Power BI Desktop, Cognitive Services, machine learning and data analytics tools, and more Architect a machine learning solution factoring in all aspects of self service, enterprise, deployment, and sharing This book is for data scientists, data analysts, developers, architects, and managers who want to leverage machine learning in their products, organization, and services, and make educated, cost-saving decisions about their ML architecture and tool set. Leila Etaati, PhD, is a Microsoft artificial intelligence and data platform MVP, speaker, trainer, and founding consultant with RADACAD where she trains and strategically advises some of today's largest global enterprises. Renowned in the field of AI and BI, she presents at many Microsoft events, including Ignite, Microsoft Data Insights Summit, PASS, and more. Leila is passionate about teaching others and resolving complex business solutions through the vast capabilities of machine learning and BI. She blogs and is author of Power BI and R through RADACAD.

ISBN: 9781484236581

Standard No.: 10.1007/978-1-4842-3658-1doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5 / .E833 2019

Dewey Class. No.: 006.31

Machine Learning with Microsoft technologies = selecting the right architecture and tools for your project /
LDR:05127nmm a2200325 a 4500 001 2192444
003 DE-He213
005 20190614021340.0
006 m d
007 cr nn 008maaau
008 200506s2019 cau s 0 eng d
020 $a 9781484236581 $q (electronic bk.)
020 $a 9781484236574 $q (paper)
024 7 $a 10.1007/978-1-4842-3658-1 $2 doi
035 $a 978-1-4842-3658-1
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5 $b .E833 2019
072 7 $a UMP $2 bicssc
072 7 $a COM051380 $2 bisacsh
072 7 $a UMP $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .E83 2019
100 1 $a Etaati, Leila. $3 3412766
245 1 0 $a Machine Learning with Microsoft technologies $h [electronic resource] : $b selecting the right architecture and tools for your project / $c by Leila Etaati.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2019.
300 $a xv, 365 p. : $b ill., digital ; $c 24 cm.
505 0 $a Part I: Getting Started -- Chapter 1: Introduction to Machine Learning -- Chapter 2: Introduction to R -- Chapter 3: Introduction to Python -- Chapter 4: R Visualization in Power BI -- Part II: Machine Learning in R and Power BI -- Chapter 5: Business Understanding -- Chapter 6: Data Wrangling for Predictive Analysis -- Chapter 7: Predictive Analysis in Power Query with R -- Chapter 8: Descriptive Analysis in Power Query with R -- Part III: Machine Learning SQL Server -- Chapter 9: Using R with SQL Server 2016 and 2017 -- Chapter 10: Azure Databricks -- Part IV: Machine Learning in Azure -- Chapter 11: R in Azure Data Lake -- Chapter 12: Azure Machine Learning Studio -- Chapter 13: Machine Learning in Azure Stream Analytics -- Chapter 14: Azure Machine Learning (ML) Workbench -- Chapter 15: Machine Learning on HDInsight -- Chapter 16: Data Science Virtual Machine and AI Framework -- Chapter 17: Deep Learning Tools with Cognitive Toolkit (CNTK) -- Part V: Data Science Virtual Machine -- Chapter 18: Cognitive Service Toolkit -- Chapter 19: Bot Framework -- Chapter 20: Overview on Microsoft Machine Learning Tools.
520 $a Know how to do machine learning with Microsoft technologies. This book teaches you to do predictive, descriptive, and prescriptive analyses with Microsoft Power BI, Azure Data Lake, SQL Server, Stream Analytics, Azure Databricks, HD Insight, and more. The ability to analyze massive amounts of real-time data and predict future behavior of an organization is critical to its long-term success. Data science, and more specifically machine learning (ML), is today's game changer and should be a key building block in every company's strategy. Managing a machine learning process from business understanding, data acquisition and cleaning, modeling, and deployment in each tool is a valuable skill set. Machine Learning with Microsoft Technologies is a demo-driven book that explains how to do machine learning with Microsoft technologies. You will gain valuable insight into designing the best architecture for development, sharing, and deploying a machine learning solution. This book simplifies the process of choosing the right architecture and tools for doing machine learning based on your specific infrastructure needs and requirements. Detailed content is provided on the main algorithms for supervised and unsupervised machine learning and examples show ML practices using both R and Python languages, the main languages inside Microsoft technologies. What You'll Learn: Choose the right Microsoft product for your machine learning solution Create and manage Microsoft's tool environments for development, testing, and production of a machine learning project Implement and deploy supervised and unsupervised learning in Microsoft products Set up Microsoft Power BI, Azure Data Lake, SQL Server, Stream Analytics, Azure Databricks, and HD Insight to perform machine learning Set up a data science virtual machine and test-drive installed tools, such as Azure ML Workbench, Azure ML Server Developer, Anaconda Python, Jupyter Notebook, Power BI Desktop, Cognitive Services, machine learning and data analytics tools, and more Architect a machine learning solution factoring in all aspects of self service, enterprise, deployment, and sharing This book is for data scientists, data analysts, developers, architects, and managers who want to leverage machine learning in their products, organization, and services, and make educated, cost-saving decisions about their ML architecture and tool set. Leila Etaati, PhD, is a Microsoft artificial intelligence and data platform MVP, speaker, trainer, and founding consultant with RADACAD where she trains and strategically advises some of today's largest global enterprises. Renowned in the field of AI and BI, she presents at many Microsoft events, including Ignite, Microsoft Data Insights Summit, PASS, and more. Leila is passionate about teaching others and resolving complex business solutions through the vast capabilities of machine learning and BI. She blogs and is author of Power BI and R through RADACAD.
650 0 $a Machine learning. $3 533906
650 1 4 $a Microsoft and .NET. $3 3134847
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Python. $3 3201289
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
856 4 0 $u https://doi.org/10.1007/978-1-4842-3658-1
950 $a Professional and Applied Computing (Springer-12059)