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:
    non sort: The
    title: data science workshop
  name:
    name part: So, Anthony
    name part: (Data scientist)
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Joseph, Thomas V.
    role:
      role term: author.
  name:
    name part: John, Robert Thas
    role:
      role term: author.
  name:
    name part: Asare, Samuel
    name part: Dr
    role:
      role term: author.
  name:
    name part: Worsley, Andrew
    role:
      role term: author.
  name:
    name part: Knovel (Firm)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: enk
    date issued: 2020
    edition: Second edition.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (1 volume) : illustrations
  abstract: Gain expert guidance on how to successfully develop machine learning models in Python and build your own unique data platformsKey Features:Gain a full understanding of the model production and deployment processBuild your first machine learning model in just five minutes and get a hands-on machine learning experienceUnderstand how to deal with common challenges in data science projectsBook Description:Where there's data, there's insight. With so much data being generated, there is immense scope to extract meaningful information that'll boost business productivity and profitability. By learning to convert raw data into game-changing insights, you'll open new career paths and opportunities.The Data Science Workshop begins by introducing different types of projects and showing you how to incorporate machine learning algorithms in them. You'll learn to select a relevant metric and even assess the performance of your model. To tune the hyperparameters of an algorithm and improve its accuracy, you'll get hands-on with approaches such as grid search and random search.Next, you'll learn dimensionality reduction techniques to easily handle many variables at once, before exploring how to use model ensembling techniques and create new features to enhance model performance. In a bid to help you automatically create new features that improve your model, the book demonstrates how to use the automated feature engineering tool. You'll also understand how to use the orchestration and scheduling workflow to deploy machine learning models in batch.By the end of this book, you'll have the skills to start working on data science projects confidently. By the end of this book, you'll have the skills to start working on data science projects confidently.What You Will Learn:Explore the key differences between supervised learning and unsupervised learningManipulate and analyze data using scikit-learn and pandas librariesUnderstand key concepts such as regression, classification, and clusteri…
  subject:
    topic: Machine learning
  subject:
    topic: Electronic data processing
  subject:
    topic: Statistics
    topic: Data processing
  subject:
    topic: Python (Computer program language)
  subject:
    topic: Application software
    topic: Development
  subject:
    topic: Apprentissage automatique
  subject:
    topic: Statistique
    topic: Informatique
  subject:
    topic: Python (Langage de programmation)
  subject:
    topic: Logiciels d'application
    topic: Développement
  subject:
    topic: Application software
    topic: Development
  subject:
    topic: Electronic data processing
  subject:
    topic: Machine learning
  subject:
    topic: Python (Computer program language)
  subject:
    topic: Statistics
    topic: Data processing
  classification: Q325.5
  classification: 006.31
  identifier: 9781800566927
  identifier: CL0501000166 Safari Books Online
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: UMI
    record creation date: 201112
    record change date: 20260121210953.9
    record identifier: in00005676433
    language of cataloging:
      language term: eng