rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Predictive Analytics with Microsoft Azure Machine Learning
    sub title: Build and Deploy Actionable Solutions in Minutes
  name:
    name part: Barga, Roger.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Fontama, Valentine.
    role:
      role term: author.
  name:
    name part: Tok, Wee Hyong.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxu
    date issued: 2014
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVI, 188 p. 116 illus. online resource.
  abstract: Data Science and Machine Learning are in high demand, as customers are increasingly looking for ways to glean insights from all their data. More customers now realize that Business Intelligence is not enough as the volume, speed and complexity of data now defy traditional analytics tools. While Business Intelligence addresses descriptive and diagnostic analysis, Data Science unlocks new opportunities through predictive and prescriptive analysis. The purpose of this book is to provide a gentle and instructionally organized introduction to the field of data science and machine learning, with a focus on building and deploying predictive models. The book also provides a thorough overview of the Microsoft Azure Machine Learning service using task oriented descriptions and concrete end-to-end examples, sufficient to ensure the reader can immediately begin using this important new service. It describes all aspects of the service from data ingress to applying machine learning and evaluating the resulting model, to deploying the resulting model as a machine learning web service. Finally, this book attempts to have minimal dependencies, so that you can fairly easily pick and choose chapters to read. When dependencies do exist, they are listed at the start and end of the chapter. The simplicity of this new service from Microsoft will help to take Data Science and Machine Learning to a much broader audience than existing products in this space. Learn how you can quickly build and deploy sophisticated predictive models as machine learning web services with the new Azure Machine Learning service from Microsoft.
  note: by Roger Barga, Valentine Fontama, Wee Hyong Tok.
  subject:
    topic: Computer science
  subject:
    topic: Database management
  subject:
    topic: Computer Science
  subject:
    topic: Database Management
  classification: QA76.9.D3
  classification: 005.74
  related item:
    title info:
      title: Springer eBooks
  identifier: 9781484204450
  identifier: http://dx.doi.org/10.1007/978-1-4842-0445-0
  record info:
    record creation date: 141125
    record change date: 20141125080219.0
    record identifier: 978-1-4842-0445-0