Microsoft Azure machine learning : explore predictive analytics using step-by-step tutorials and build models to make prediction in a jiffy with a few mouse clicks / Sumit Mund.

Format:

Book

Author/Creator:

Mund, Sumit, author.

Series:

Professional expertise distilled

Language:

English

Subjects (All):

Windows Azure.

Information technology--Management.

Information technology.

Physical Description:

1 online resource (212 pages)

Edition:

1st edition

Place of Publication:

Birmingham, [England] ; Mumbai, [India] : Packt Publishing, June 2015

Language Note:

English

System Details:

text file

Summary:

The book is intended for those who want to learn how to use Azure Machine Learning. Perhaps you already know a bit about Machine Learning, but have never used ML Studio in Azure; or perhaps you are an absolute newbie. In either case, this book will get you up-and-running quickly.

Contents:

Cover; Copyright; Credits; About the Author; Acknowledgments; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Introduction; Introduction to predictive analytics; Problem definition and scoping; Data collection; Data exploration and preparation; Model development; Model deployment; Machine learning; Kinds of machine learning problems; Classification; Regression; Clustering; Common machine learning techniques/algorithms; Linear regression; Logistic regression; Decision tree-based ensemble models; Neural networks and deep learning

Introduction to Azure Machine LearningML Studio; Summary; Chapter 2: ML Studio Inside Out; Introduction to ML Studio; Getting started with Microsoft Azure; Microsoft account and subscription; Creating and managing ML workspaces; Inside ML Studio; Experiments; Creating and editing an experiment; Running an experiment; Creating and running an experiment - do it yourself; Workspace as a collaborative environment; Summary; Chapter 3: Data Exploration and Visualization; The basic concepts; The mean; The median; Standard deviation and variance; Understanding a histogram; The box and whiskers plot

The outliersA scatter plot; Data exploration in ML Studio; Visualizing an automobile price dataset; A histogram; The box and whiskers plot; Comparing features; A snapshot; Do it yourself; Summary; Chapter 4: Getting Data in and out of ML Studio; Getting data in ML Studio; Uploading data from a PC; The Enter Data module; The Data Reader module; Getting data from the Web; Getting data from Azure; Data format conversion; Getting data from ML Studio; Saving dataset in a PC; Saving results in ML Studio; The Writer module; Summary; Chapter 5: Data Preparation; Data manipulation; Clean Missing Data

Removing duplicate rowsProject columns; The Metadata Editor module; The Add Columns module; The Add Rows module; The Join module; Splitting data; Do it yourself; The Apply SQL Transformation module; Advanced data preprocessing; Removing outliers; Data normalization; The Apply Math Operation module; Feature selection; The Filter Based Feature Selection module; The Fisher Linear Discriminant Analysis module; Data preparation beyond ready-made modules; Summary; Chapter 6: Regression Models; Understanding regression algorithms; Train, score, and evaluate; The test and train dataset; Evaluating

The mean absolute errorThe root mean squared error; The relative absolute error; The relative squared error; The coefficient of determination; Linear regression; Optimizing parameters for a learner - the sweep parameters module; The decision forest regression; The train neural network regression - do it yourself; Comparing models with the evaluate model; Comparing models - the neural network and boosted decision tree; Other regression algorithms; No free lunch; Summary; Chapter 7: Classification Models; Understanding classification; Evaluation metrics; True positive; False positive

True negative

Notes:

Includes index.

"Professional expertise distilled"--Cover.

Description based on online resource; title from PDF title page (ebrary, viewed June 30, 2015).

ISBN:

9781784398514

1784398519

OCLC:

915143484