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
Who 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.
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