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Course
- AI
- Cloud
- Data
Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure
In this course, you'll learn how to prepare, clean up, and engineer new features from the data with Azure Machine Learning, so the dataset can be represented in a form that's easy for the learning algorithm to learn the patterns.
What you'll learn
Data comes from many different sources. So when you join them, they are naturally inconsistent. In this course, Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure, you will be taken on a journey where you begin with data that's unsuitable for machine learning and use different modules in Azure Machine Learning to clean and preprocess the data. First, you will learn how to set up the data and workspace in Azure Machine Learning. Next, you will discover the role of feature engineering in machine learning. Finally, you will explore how to Identify specific data-level issues for machine learning models. When you’re finished with this course, you will have a clean dataset processed with azure machine learning modules that’s ready to build production-ready machine learning models.
Table of contents
- Introduction | 2m 35s
- What Is Machine Learning? | 3m 12s
- Introduction to Azure Machine Learning | 2m 27s
- Azure Machine Learning Experiment Workflow | 2m 11s
- Prerequisites | 30s
- Demo: Creating an Azure Machine Learning Studio Workspace | 4m 1s
- Demo: Creating an Azure Machine Learning Service Workspace | 2m 22s
- Demo: Exploring the Dataset | 3m
- Summary | 52s
About the author
Ravikiran is an independent cloud consultant and author focused on developing solutions in Microsoft Azure. His interests include everything in the cloud space, DevOps and Machine Learning with contributions in domains like Healthcare, Banking and Web Analytics. He works at the intersection of education and technology.
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