| Description |
1 online resource (1 volume) : illustrations |
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text file |
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PDF |
| Series |
Professional expertise distilled |
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Professional expertise distilled.
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| Note |
Includes index. |
| 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 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. |
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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. |
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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. |
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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. |
| 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. |
| Subject |
Windows Azure.
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Windows Azure |
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Machine learning.
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Data mining.
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Apprentissage automatique. |
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Exploration de données (Informatique) |
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Data mining |
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Machine learning |
| Other Form: |
Print version: Mund, Sumit. 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. Birmingham, [England] ; Mumbai, [India] : Packt Publishing, ©2015 xi, 183 pages 9781784390792 |
| ISBN |
9781784398514 (electronic bk.) |
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1784398519 (electronic bk.) |
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