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 uses in R
  name:
    name part: Ramasubramanian, Karthik
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Singh, Abhishek
    name part: 1976-
    role:
      role term: author.
  type of resource: text
  genre: elektronické knihy
  genre: electronic books
  origin info:
    place:
      place term: nyu
    date issued: 2019
    edition: Second edition.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (xxiv, 700 pages)
  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 and R -- Chapter 2: Data Exploration and Preparation -- Chapter 3: Sampling and Resampling Techniques -- Chapter 4: Data Visualization in R -- Chapter 5: Feature Engineering -- Chapter 6: Machine Learning 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: Deep Learning Models using Keras and TensorFlow.
  note: Karthik Ramasubramanian, Abhishek Singh.
  note: Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty
  subject:
    topic: Machine learning
  subject:
    topic: R (Computer program language)
  related item:
    title info:
      title: Machine Learning Using R : With Time Series and Industry-Based Use Cases in R
    name:
      name part: Ramasubramanian, Karthik.
    origin info:
      publisher: Berkeley, CA : Apress L.P., ©2018
  identifier: 9781484242155
  identifier: 1484242157
  identifier: 9781484242148
  identifier: 1484242149
  identifier: https://proxy.k.utb.cz/login
  access condition: Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty
  record info:
    record content source: EBLCP
    record creation date: 181222
    record change date: 20240717213016.0
    record identifier: kn-on1080081706
    language of cataloging:
      language term: eng