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: Mastering Machine Learning with Python in Six Steps
    sub title: A Practical Implementation Guide to Predictive Data Analytics Using Python
  name:
    name part: Swamynathan, Manohar.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxu
    date issued: 2019
    edition: 2nd ed. 2019.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVII, 457 p. 185 illus., 1 illus. in color. online resource.
  abstract: Explore fundamental to advanced Python 3 topics in six steps, all designed to make you a worthy practitioner. This updated version's approach is based on the "six degrees of separation" theory, which states that everyone and everything is a maximum of six steps away and presents each topic in two parts: theoretical concepts and practical implementation using suitable Python 3 packages. You'll start with the fundamentals of Python 3 programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as exploratory analysis, feature dimension reduction, regressions, time series forecasting and their efficient implementation in Scikit-learn are covered as well. You'll also learn commonly used model diagnostic and tuning techniques. These include optimal probability cutoff point for class creation, variance, bias, bagging, boosting, ensemble voting, grid search, random search, Bayesian optimization, and the noise reduction technique for IoT data. Finally, you'll review advanced text mining techniques, recommender systems, neural networks, deep learning, reinforcement learning techniques and their implementation. All the code presented in the book will be available in the form of iPython notebooks to enable you to try out these examples and extend them to your advantage.
  table of contents: Chapter 1: Step 1 - Getting Started with Python -- Chapter 2 : Step 2 - Introduction to Machine Learning -- Chapter 3: Step 3 - Fundamentals of Machine Learning -- Chapter 4: Step 4 - Model Diagnosis and Tuning -- Chapter 5: Step 5 - Text Mining, NLP AND Recommender Systems -- Chapter 6: Step 6 - Deep and Reinforcement Learning -- Chapter 7 : Conclusion.
  note: by Manohar Swamynathan.
  subject:
    topic: Artificial intelligence
  subject:
    topic: Big data
  subject:
    topic: Open source software
  subject:
    topic: Computer programming
  subject:
    topic: Artificial Intelligence
  subject:
    topic: Big Data
  subject:
    topic: Open Source
  classification: Q334-342
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  identifier: 9781484249475
  identifier: https://doi.org/10.1007/978-1-4842-4947-5
  record info:
    record creation date: 191001
    record change date: 20191002141533.0
    record identifier: 978-1-4842-4947-5