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.
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.
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.
Course Overview Hi, everyone! I am Ravikiran, and this is my Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure course. Azure Machine Learning is a fully-interactive drag-and-drop visual interface that lets you build machine learning models. With Azure machine learning, you can use different modules to clean and process your data for building predictive models. In this course, we will do data preparation and feature engineering with Azure Machine Learning and process the raw data into one suitable for machine learning models. Some of the major topics that we will cover include setting up the data and workspace in Azure Machine Learning, doing data preprocessing tasks in Azure Machine Learning, techniques to handle missing data, the role of feature engineering in machine learning, different ways to split your data, and identifying specific data-level issues for machine learning models. By the end of this course, you will have a clean dataset processed with Azure Machine Learning modules that's ready to build production ready machine learning models. But before you begin, make sure you have at least a little familiarity with some statistical concepts and Azure fundamentals.