Machine Learning with Microsoft Technologies : Selecting the Right Architecture and Tools for Your Project / by Leila Etaati.

Format:

Book

Author/Creator:

Etaati, Leila., Author.

Language:

English

Subjects (All):

Microsoft software.

Microsoft .NET Framework.

Artificial intelligence.

Python (Computer program language).

Microsoft and .NET.

Artificial Intelligence.

Python.

Local Subjects:

Microsoft and .NET.

Artificial Intelligence.

Python.

Physical Description:

1 online resource (XV, 365 p. 365 illus., 356 illus. in color.)

Edition:

1st ed. 2019.

Place of Publication:

Berkeley, CA : Apress : Imprint: Apress, 2019.

System Details:

text file

Summary:

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.

Contents:

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.

Notes:

Includes bibliographical references.

ISBN:

9781484236581

1484236580

OCLC:

1108874744