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: Supervised Learning with Python
    sub title: Concepts and Practical Implementation Using Python
  name:
    name part: Verdhan, Vaibhav
    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: 2020
    edition: 1st edition.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (387 pages)
  abstract: Gain a thorough understanding of supervised learning algorithms by developing use cases with Python. You will study supervised learning concepts, Python code, datasets, best practices, resolution of common issues and pitfalls, and practical knowledge of implementing algorithms for structured as well as text and images datasets. You'll start with an introduction to machine learning, highlighting the differences between supervised, semi-supervised and unsupervised learning. In the following chapters you'll study regression and classification problems, mathematics behind them, algorithms like Linear Regression, Logistic Regression, Decision Tree, KNN, Naïve Bayes, and advanced algorithms like Random Forest, SVM, Gradient Boosting and Neural Networks. Python implementation is provided for all the algorithms. You'll conclude with an end-to-end model development process including deployment and maintenance of the model.After reading Supervised Learning with Python you'll have a broad understanding of supervised learning and its practical implementation, and be able to run the code and extend it in an innovative manner. What You'll Learn Review the fundamental building blocks and concepts of supervised learning using Python Develop supervised learning solutions for structured data as well as text and images Solve issues around overfitting, feature engineering, data cleansing, and cross-validation for building best fit models Understand the end-to-end model cycle from business problem definition to model deployment and model maintenance Avoid the common pitfalls and adhere to best practices while creating a supervised learning model using Python Who This Book Is For Data scientists or data analysts interested in best practices and standards for supervised learning, and using classification algorithms and regression techniques to develop predictive models.
  note: Verdhan, Vaibhav.
  note: Electronic resource.
  note: Electronic reproduction. Boston, MA : Safari, Available via World Wide Web. 2020.
  note: Mode of access: World Wide Web.
  note: Made available through: Safari, an O'Reilly Media Company.
  identifier: 9781484261569
  identifier: https://proxy.library.tamu.edu/login
  record info:
    record creation date: 081020
    record change date: 20260129204151.9
    record identifier: in00004442620