Course Description
This course covers introductory concepts of Data Science using Microsoft Azure Machine Learning (ML). The course explores the features of Microsoft Azure Machine Learning, with big data tools such as HDInsight and R Services, for analyzing and presenting data. It also explains how to use data processing algorithms such as regression, neural networks, classification, and clustering algorithms with Azure ML.
Course Objectives
After successful completion of this course, participants will be able to:
- Explain what Machine Learning (ML), algorithms and languages are
- Explain the purpose of Azure ML
- Use the features of Azure ML Studio to upload and explore data in Azure ML.
- Prepare datasets for use with Azure ML.
- Use algorithms like (regression, neural networks, classification, and clustering) with Azure ML
- Explain the use of cognitive services APIs with Azure ML
- Explain how HDInsight is used with Azure ML.
- Use R and Python with Azure ML and choose when to use a particular language.
- Explain how to deploy and configure SQL Server and support R services.
Audience
This course is intended for Data scientists analysts getting started with Microsoft Azure Machine Learning. The course can also benefit Data and Business Analysts, Database and other professionals involved in creating ML modules. Experience in R or Python programming or background in statistics is helpful, but not required.
Curriculum
- 13 Sections
- 38 Lessons
- 10 Weeks
- Introduction to Machine Learning3
- Introduction to Azure Machine Learning3
- Managing Datasets3
- Preparing Data for use with Azure ML2
- Using Feature Engineering and Selection2
- Building Azure ML Models4
- Classification and Clustering with Azure ML models3
- Using R and Python with Azure ML3
- Initializing and Optimizing ML Models3
- Using Azure ML Models2
- Using Cognitive Services4
- Using ML with HDInsight3
- Using R Services with ML3