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 3m
- What Is Machine Learning? 3m
- Introduction to Azure Machine Learning 2m
- Azure Machine Learning Experiment Workflow 2m
- Prerequisites 1m
- Demo: Creating an Azure Machine Learning Studio Workspace 4m
- Demo: Creating an Azure Machine Learning Service Workspace 2m
- Demo: Exploring the Dataset 3m
- Summary 1m
- Introduction 1m
- Data Preprocessing Methods 0m
- Demo: Exploratory Data Analysis 6m
- Demo: Data Cleaning (Erroneous Data) 2m
- Demo: Data Cleaning (Outliers) 2m
- Demo: Data Cleaning (Duplicate Rows) 1m
- Demo: Data Transformation 4m
- Demo: Reducing Data (Record Sampling) 2m
- Demo: Reducing Data (Attribute Sampling) 1m
- Demo: Discretizing Data 3m
- Entropy-based Discretization 3m
- Demo: Entropy-based Discretization 1m
- Summary 1m
- Introduction 1m
- Why Feature Engineering? 2m
- Role of Feature Engineering in Model Complexity 2m
- Build Better Models with Feature Engineering 2m
- Feature Engineering Numeric Variables 2m
- Feature Engineering Categorical Variables 2m
- Demo: One-hot Encoding Categorical Variables 4m
- Demo: Learning with Counts Categorical Variables 4m
- Summary 1m
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 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