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: Machine Learning Using R
    sub title: With Time Series and Industry-Based Use Cases in R
  name:
    name part: Ramasubramanian, Karthik.
    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: Singh, Abhishek.
    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: XXIV, 700 p. 233 illus., 24 illus. in color. online resource.
  abstract: Examine the latest technological advancements in building a scalable machine-learning model with big data using R. This second edition shows you how to work with a machine-learning algorithm and use it to build a ML model from raw data. You will see how to use R programming with TensorFlow, thus avoiding the effort of learning Python if you are only comfortable with R. As in the first edition, the authors have kept the fine balance of theory and application of machine learning through various real-world use-cases which gives you a comprehensive collection of topics in machine learning. New chapters in this edition cover time series models and deep learning. You will: Understand machine learning algorithms using R Master the process of building machine-learning models Cover the theoretical foundations of machine-learning algorithms See industry focused real-world use cases Tackle time series modeling in R Apply deep learning using Keras and TensorFlow in R.
  table of contents: Chapter 1: Introduction to Machine Learning -- Chapter 2: Data Exploration and Preparation -- Chapter 3: Sampling and Resampling Techniques -- Chapter 4: Visualization of Data -- Chapter 5: Feature Engineering -- Chapter 6: Machine Learning Models: Theory and Practice -- Chapter 7: Machine Learning Model Evaluation -- Chapter 8: Model Performance Improvement -- Chapter 9: Time Series Modelling -- Chapter 10: Scalable Machine Learning and related technology -- Chapter 11: Introduction to Deep Learning Models using Keras and TensorFlow.
  note: by Karthik Ramasubramanian, Abhishek Singh.
  subject:
    topic: Artificial intelligence
  subject:
    topic: Open source software
  subject:
    topic: Computer programming
  subject:
    topic: Programming languages (Electronic computers)
  subject:
    topic: Artificial Intelligence
  subject:
    topic: Open Source
  subject:
    topic: Programming Languages, Compilers, Interpreters
  classification: Q334-342
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  identifier: 9781484242155
  identifier: https://doi.org/10.1007/978-1-4842-4215-5
  record info:
    record creation date: 181212
    record change date: 20191024151350.0
    record identifier: 978-1-4842-4215-5