About this course
The past can often be the key to predicting the future. Big data from historical sources is a valuable resource for identifying trends and building machine learning models that apply statistical patterns and predict future outcomes.
This course introduces Azure Machine Learning, and explores techniques and considerations for using it to build models from big data sources, and to integrate predictive insights into big data processing workflows.
Please Note: Learners who successfully complete this course can earn a CloudSwyft digital certificate and skill badge - these are detailed, secure and blockchain authenticated credentials that profile the knowledge and skills you’ve acquired in this course.
What you'll learn
- How to create predictive web services with Azure Machine Learning
- How to work with big data sources in Azure Machine Learning
- How to integrate Azure Machine Learning into big data batch processing pipelines
- How to integrate Azure Machine Learning into real-time big data processing solutions
Course Syllabus
Module 1: Introduction to Azure Machine Learning
Module 2: Building Predictive Models with Azure Machine Learning
Module 3: Operationalizing Machine Learning Models
Module 4: Using Azure Machine Learning in Big Data Solutions
Meet the instructors
Graeme Malcolm
Senior Content Developer
Microsoft Learning Experiences
Graeme has been a trainer, consultant, and author for longer than he cares to remember, specializing in SQL Server and the Microsoft data platform. He is a Microsoft Certified Solutions Expert for the SQL Server Data Platform and Business Intelligence. After years of working with Microsoft as a partner and vendor, he now works in the Microsoft Learning Experiences team as a senior content developer, where he plans and creates content for developers and data professionals who want to get the best out of Microsoft technologies.
Dr. Steve Elston
Managing Director
Quantia Analytics, LLC
Steve is a big data geek and data scientist, with over two decades of experience using R and S/SPLUS for predictive analytics and machine learning. He holds a PhD degree in Geophysics from Princeton University, and has led multi-national data science teams across various companies
Note: The practical elements of this course are based on Microsoft Azure, and require an Azure subscription. Instructions for signing up for a free trial subscription are provided with the course materials, or you can use an existing Azure subscription if you have one.
Learn more here about Azure
