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.
| Otros Autores: | |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham, [England] ; Mumbai, [India] :
Packt Publishing
2015.
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| Edición: | 1st edition |
| Colección: | Professional expertise distilled
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009629786306719 |
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 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
