Before you do any analysis and find results from your dataset, you need to understand the data. You can do so by data exploration and visualization. ML Studio provides a very basic option to do so, but with the most essential information. To use the tool and understand the data, you need to be familiar with some of the basic concepts such as mean, standard deviation, variables, or features in a dataset, and basic plotting techniques, such as histogram, box plot, scatter plot, and so on. The first part of this chapter will familiarize you with these concepts and then you will find the use of these inside ML Studio to apply them to a sample dataset. If you are a practitioner or are familiar with statistics, feel free to skip the basic concepts section and move on to the next.
Microsoft Azure Machine Learning
By :
Microsoft Azure Machine Learning
By:
Overview of this book
Table of Contents (21 chapters)
Microsoft Azure Machine Learning
Credits
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
Introduction
ML Studio Inside Out
Data Exploration and Visualization
Getting Data in and out of ML Studio
Data Preparation
Regression Models
Classification Models
Clustering
A Recommender System
Extensibility with R and Python
Publishing a Model as a Web Service
Case Study Exercise I
Case Study Exercise II
Index
Customer Reviews