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: Python for Probability, Statistics, and Machine Learning
  name:
    name part: Unpingco, José.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2016
    edition: 1st ed. 2016.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XV, 276 p. 110 illus., 7 illus. in color. online resource.
  abstract: This book covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. The entire text, including all the figures and numerical results, is reproducible using the Python codes and their associated Jupyter/IPython notebooks, which are provided as supplementary downloads. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Modern Python modules like Pandas, Sympy, and Scikit-learn are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples. This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python programming. Explains how to simulate, conceptualize, and visualize random statistical processes and apply machine learning methods; Connects to key open-source Python communities and corresponding modules focused on the latest developments in this area; Outlines probability, statistics, and machine learning concepts using an intuitive visual approach, backed up with corresponding visualization codes.
  table of contents: Getting Started with Scientific Python -- Probability -- Statistics -- Machine Learning -- Notation.
  note: by José Unpingco.
  subject:
    topic: Engineering
  subject:
    topic: Mathematical statistics
  subject:
    topic: Data mining
  subject:
    topic: Statistics
  subject:
    topic: Applied mathematics
  subject:
    topic: Engineering mathematics
  subject:
    topic: Electrical engineering
  subject:
    topic: Engineering
  subject:
    topic: Communications Engineering, Networks
  subject:
    topic: Appl.Mathematics/Computational Methods of Engineering
  subject:
    topic: Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
  subject:
    topic: Probability and Statistics in Computer Science
  subject:
    topic: Data Mining and Knowledge Discovery
  classification: TK1-9971
  classification: 621.382
  related item:
    title info:
      title: Springer eBooks
  identifier: 9783319307176
  identifier: http://dx.doi.org/10.1007/978-3-319-30717-6
  record info:
    record creation date: 160316
    record change date: 20171223021245.0
    record identifier: 978-3-319-30717-6