LEADER 06864nam 22007815 450 001 9910300644603321 005 20200629154024.0 010 $a9781484212004 010 $a1484212002 024 7 $a10.1007/978-1-4842-1200-4 035 $a(CKB)3710000000467621 035 $a(EBL)4178070 035 $a(SSID)ssj0001546885 035 $a(PQKBManifestationID)16141363 035 $a(PQKBTitleCode)TC0001546885 035 $a(PQKBWorkID)14796272 035 $a(PQKB)10930942 035 $a(DE-He213)978-1-4842-1200-4 035 $a(MiAaPQ)EBC4178070 035 $a(iGPub)SPNA0036491 035 $a(PPN)188457399 035 $a(OCoLC)924210404 035 $a(OCoLC)ocn924210404 035 $a(CaSebORM)9781484212004 035 $a(EXLCZ)993710000000467621 100 $a20150826d2015 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aPredictive Analytics with Microsoft Azure Machine Learning 2nd Edition /$fby Valentine Fontama, Roger Barga, Wee Hyong Tok 205 $a2nd ed. 2015. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2015. 215 $a1 online resource (303 p.) 300 $aIncludes index. 311 08$a9781484212011 311 08$a1484212010 320 $aIncludes bibliographical references and index. 327 $aContents at a Glance; Contents; About the Authors; About the Technical Reviewers; Acknowledgments; Foreword; Introduction; Part I: Introducing Data Science and Microsoft Azure Machine Learning ; Chapter 1: Introduction to Data Science; What is Data Science?; Analytics Spectrum ; Descriptive Analysis; Diagnostic Analysis; Predictive Analysis; Prescriptive Analysis; Why Does It Matter and Why Now?; Data as a Competitive Asset ; Increased Customer Demand ; Increased Awareness of Data Mining Technologies ; Access to More Data; Faster and Cheaper Processing Power 327 $aThe Data Science Process Common Data Science Techniques ; Classification Algorithms; Clustering Algorithms ; Regression Algorithms; Simulation ; Content Analysis; Recommendation Engines ; Cutting Edge of Data Science; The Rise of Ensemble Models; Real-World Applications of Ensemble Models; Building an Ensemble Model; Summary; Bibliography; Chapter 2: Introducing Microsoft Azure Machine Learning; Hello, Machine Learning Studio!; Components of an Experiment; Introducing the Gallery; Five Easy Steps to Creating a Training Experiment; Step 1: Getting the Data 327 $aStep 2: Preprocessing the Data Step 3: Defining the Features; Step 4: Choosing and Applying Machine Learning Algorithms ; Step 5: Predicting Over New Data; Deploying Your Model in Production; Creating a Predictive Experiment ; Publishing Your Experiment as a Web Service; Accessing the Azure Machine Learning Web Service ; Summary; Chapter 3: Data Preparation; Data Cleaning and Processing; Getting to Know Your Data; Missing and Null Values; Handling Duplicate Records; Identifying and Removing Outliers; Feature Normalization; Dealing with Class Imbalance; Feature Selection 327 $aFeature Engineering Binning Data; The Curse of Dimensionality; Summary; Chapter 4: Integration with R; R in a Nutshell ; Building and Deploying Your First R Script; Using R for Data Preprocessing ; Using a Script Bundle (ZIP) ; Building and Deploying a Decision Tree Using R; Summary; Chapter 5: Integration with Python; Overview ; Python Jumpstart ; Using Python in Azure ML Experiments ; Using Python for Data Preprocessing ; Combining Data using Python; Handling Missing Data Using Python; Feature Selection Using Python; Running Python Code in an Azure ML Experiment; Summary 327 $aPart II: Statistical and Machine Learning Algorithms Chapter 6: Introduction to Statistical and Machine Learning Algorithms; Regression Algorithms; Linear Regression ; Neural Networks ; Decision Trees ; Boosted Decision Trees; Classification Algorithms ; Support Vector Machines ; Bayes Point Machines ; Clustering Algorithms ; Summary; Part III: Practical Applications ; Chapter 7: Building Customer Propensity Models; The Business Problem ; Data Acquisition and Preparation ; Data Analysis; More Data Treatment; Feature Selection; Training the Model; Model Testing and Validation 327 $aModel Performance 330 $aPredictive Analytics with Microsoft Azure Machine Learning, Second Edition is a practical tutorial introduction to the field of data science and machine learning, with a focus on building and deploying predictive models. The book provides a thorough overview of the Microsoft Azure Machine Learning service released for general availability on February 18th, 2015 with practical guidance for building recommenders, propensity models, and churn and predictive maintenance models. The authors use task oriented descriptions and concrete end-to-end examples to ensure that the reader can immediately begin using this new service. The book describes all aspects of the service from data ingress to applying machine learning, evaluating the models, and deploying them as web services. Learn how you can quickly build and deploy sophisticated predictive models with the new Azure Machine Learning from Microsoft. What?s New in the Second Edition? Five new chapters have been added with practical detailed coverage of: Python Integration ? a new feature announced February 2015 Data preparation and feature selection Data visualization with Power BI Recommendation engines Selling your models on Azure Marketplace. 606 $aArtificial intelligence 606 $aSoftware engineering 606 $aData mining 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aSoftware Engineering/Programming and Operating Systems$3https://scigraph.springernature.com/ontologies/product-market-codes/I14002 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 615 0$aArtificial intelligence. 615 0$aSoftware engineering. 615 0$aData mining. 615 14$aArtificial Intelligence. 615 24$aSoftware Engineering/Programming and Operating Systems. 615 24$aData Mining and Knowledge Discovery. 676 $a004 700 $aFontama$b Valentine$4aut$4http://id.loc.gov/vocabulary/relators/aut$0969213 702 $aBarga$b Roger$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aTok$b Wee Hyong$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bUMI 801 1$bUMI 906 $aBOOK 912 $a9910300644603321 996 $aPredictive Analytics with Microsoft Azure Machine Learning 2nd Edition$92202152 997 $aUNINA