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: Machine Learning with R - Second Edition
  name:
    name part: Lantz, Brett
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Safari, an O'Reilly Media Company
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: xx
    date issued: 2015
    edition: 2nd edition.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (452 pages)
  abstract: Discover how to build machine learning algorithms, prepare data, and dig deep into data prediction techniques with R In Detail Updated and upgraded to the latest libraries and most modern thinking, Machine Learning with R, Second Edition provides you with a rigorous introduction to this essential skill of professional data science. Without shying away from technical theory, it is written to provide focused and practical knowledge to get you building algorithms and crunching your data, with minimal previous experience. With this book, you'll discover all the analytical tools you need to gain insights from complex data and learn how to choose the correct algorithm for your specific needs. Through full engagement with the sort of real-world problems data-wranglers face, you'll learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, market analysis, and clustering. What You Will Learn Harness the power of R to build common machine learning algorithms with real-world data science applications Get to grips with R techniques to clean and prepare your data for analysis, and visualize your results Discover the different types of machine learning models and learn which is best to meet your data needs and solve your analysis problems Classify your data with Bayesian and nearest neighbor methods Predict values by using R to build decision trees, rules, and support vector machines Forecast numeric values with linear regression, and model your data with neural networks Evaluate and improve the performance of machine learning models Learn specialized machine learning techniques for text mining, social network data, big data, and more Downloading the example code for this book. You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-maile…
  note: Lantz, Brett.
  note: Electronic resource.
  note: Electronic reproduction. Boston, MA : Safari, Available via World Wide Web.
  note: Mode of access: World Wide Web.
  identifier: 9781784393908
  identifier: 9781784394523
  identifier: https://proxy.library.tamu.edu/login
  record info:
    record creation date: 270116
    record change date: 20260130164649.8
    record identifier: in00004254186