Machine Learning with Microsoft Technologies

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 skillset.

Machine Learning with Microsoft Technologies is a demo driven book that explains how to do machine learning with Microsoft technologies. You will learn how to do predictive, descriptive, and prescriptive analyses with popular technologies such as Microsoft Power BI, Azure Data Lake, SQL Server, Stream Analytics, Spark, HD insight and more. You will gain valuable insight into designing the best architecture for development, sharing, and deploying a Machine Learning solution, and get familiar with the Data Science Virtual Machine. Greatly distilled, this book simplifies the process of choosing the right architecture and tools for doing machine learning based on your specific infrastructure needs and requirements.

Readers will appreciate detailed context on the main algorithms for supervised and unsupervised machine learning, and examples of ML practices using both R and Python languages, the main languages inside Microsoft technologies. They will also learn the pros and cons of each language as it relates to a given product.

What You Will Learn:

• Choose the right Microsoft product for your ML solution
• Create and manage Microsoft's tool environments for Development, Test, and Production of a Machine Learning project
• Implement and deploy supervised and unsupervised learning
• Learn to set up SQL Server, Power BI, Azure Data Lake, HDInsight, Stream Analytics 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, Machine Learning and Data Analytics tools, and more
• Use R or Python with the aim of predictive, descriptive, and prescriptive analysis
• 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.