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.
Author Name: Ravikiran Srinivasulu
Author Description:
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 is very passionate about the latest and futuristic technologies and constantly updates himself with the current technology trends. He works at the intersection of education and technology. In spare time, he likes going on long road tr… more

Table of Contents

• Course Overview
  1min
• Getting Started with Azure Machine Learning
  21mins
• Differentiating Data, Features, Targets, and Models
  17mins
• Preparing Input Data for Machine Learning Models
  25mins
• Handling Missing Data
  17mins
• Role of Feature Engineering in Machine Learning
  19mins
• Split a Data Set into Training and Testing Subsets
  21mins
• Identify Data-level Issues In Machine Learning Models
  14mins