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 /

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.

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Mund, Sumit (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham, UK : Packt Publishing, 2015.
Colección:Professional expertise distilled.
Temas:
Windows Azure.
Windows Azure
Machine learning.
Data mining.
Apprentissage automatique.
Exploration de données (Informatique)
COMPUTERS > Database Management > Data Warehousing.
Data mining
Machine learning
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • 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 Learning
  • ML 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 outliers
  • A 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 rows
  • Project 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 error
  • The 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
  • False negative
  • Accuracy
  • Precision
  • Recall
  • The F1 score
  • Threshold
  • Understanding ROC and AUC
  • Motivation for the matrix to consider
  • Training, scoring, and evaluating modules
  • Classifying diabetes or not
  • Two-class bayes point machine
  • Two-class neural network with parameter sweeping
  • Predicting adult income with decision-tree-based models
  • Do it yourself
  • comparing models to choose the best
  • Multiclass classification
  • Evaluation metrics
  • multiclass classification.
  • Multiclass classification with the Iris dataset
  • Multiclass decision forest
  • Comparing models
  • multiclass decision forest and logistic regression
  • Multiclass classification with the Wine dataset
  • Multiclass neural network with parameter sweep
  • Do it yourself
  • multiclass decision jungle
  • Summary
  • Chapter 8: Clustering
  • Understanding the K-means clustering algorithm
  • Creating a K-means clustering model using ML Studio
  • Do it yourself
  • Clustering versus classification
  • Summary
  • Chapter 9: A Recommender System
  • The Matchbox recommender
  • Kinds of recommendations
  • Understanding the recommender modules
  • The train Matchbox recommender
  • The score matchbox recommender
  • The evaluate recommender
  • Building a recommendation system
  • Summary
  • Chapter 10: Extensibility with R and Python
  • Introduction to R
  • Introduction to Python
  • Why should you extend through R/Python code?
  • Extending experiments using the Python language
  • Understanding the Execute Python Script module
  • Creating visualizations using Python
  • A simple time series analysis with the Python script
  • Importing the existing Python code
  • Do it yourself
  • Python
  • Extending experiments using the R language
  • Understanding the Execute R Script module
  • A simple time series analysis with the R script
  • Importing an existing R code
  • Including an R package
  • Understanding the Create R Model module
  • Do it yourself
  • R
  • Summary
  • Chapter 11: Publishing a Model as a Web Service
  • Preparing an experiment to be published
  • Saving a trained model
  • Creating a scoring experiment
  • Specifying the input and output of the web service
  • Publishing a model as a web service
  • Visually testing a web service
  • Consuming a published web service
  • Web service configuration
  • Updating the web service
  • Summary
  • Chapter 12: Case Study Exercise I.
  • Problem definition and scope
  • The dataset
  • Data exploration and preparation
  • Feature selection
  • Model development
  • Model deployment
  • Summary
  • Chapter 13: Case Study Exercise II
  • Problem definition and scope
  • The dataset
  • Data exploration and preparation
  • Model development
  • Model deployment
  • Summary
  • Index.

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