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 machine learning by example
    sub title: build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn
  name:
    name part: Liu, Yuxi (Hayden)
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Knovel (Firm)
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: enk
    date issued: 2020
    edition: 3rd edition.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource
  abstract: A comprehensive guide to get you up to speed with the latest developments of practical machine learning with Python and upgrade your understanding of machine learning (ML) algorithms and techniquesKey FeaturesDive into machine learning algorithms to solve the complex challenges faced by data scientists todayExplore cutting edge content reflecting deep learning and reinforcement learning developmentsUse updated Python libraries such as TensorFlow, PyTorch, and scikit-learn to track machine learning projects end-to-endBook DescriptionPython Machine Learning By Example, Third Edition serves as a comprehensive gateway into the world of machine learning (ML). With six new chapters, on topics including movie recommendation engine development with Naïve Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing images of clothing with convolutional neural networks, predicting with sequences using recurring neural networks, and leveraging reinforcement learning for making decisions, the book has been considerably updated for the latest enterprise requirements. At the same time, this book provides actionable insights on the key fundamentals of ML with Python programming. Hayden applies his expertise to demonstrate implementations of algorithms in Python, both from scratch and with libraries. Each chapter walks through an industry-adopted application. With the help of realistic examples, you will gain an understanding of the mechanics of ML techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP. By the end of this ML Python book, you will have gained a broad picture of the ML ecosystem and will be well-versed in the best practices of applying ML techniques to solve problems.What you will learnUnderstand the important concepts in ML and data scienceUse Python to explore the world of data mining and analyticsScale up model training using varied d…
  table of contents: Python Machine Learning by Example: Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn
  note: Yuxi (Hayden) Liu.
  note: Includes bibliographical references and index.
  subject:
    topic: Python (Computer program language)
  subject:
    topic: Machine learning
  subject:
    topic: Python (Langage de programmation)
  subject:
    topic: Apprentissage automatique
  subject:
    topic: Machine learning
  subject:
    topic: Python (Computer program language)
  classification: QA76.73.P98 L58 2020
  classification: 005.133
  related item:
    title info:
      title: Expert insight
  identifier: 1800203861
  identifier: 9781800203860
  identifier: 9781523136391
  identifier: 1523136391
  identifier: 9781800203860 Packt Publishing
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: YDX
    record creation date: 201104
    record change date: 20260121210953.7
    record identifier: in00005676431
    language of cataloging:
      language term: eng