Handling Big Datasets For Machine Learning

@article{1701379,
      author = {A. K. Sreeja, Prema Jain},
      title = {Handling Big Datasets For Machine Learning},
      journal = {Iconic Research And Engineering Journals},
      year = {2019},
      volume = {3},
      number = {1},
      pages = {176-180},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1701379.pdf},
      abstract = {Machine learning with Big Data is, in many ways, different than "regular" machine learning. Big Data is no longer buzzword terminology or cutting edge conceptually; rather, it just is. Big Data is not easily or precisely definable, but it is generally easy to identify when you see it. We are faced with a torrent of data generated and captured in digital form as a result of the advancement of sciences, engineering and technologies, and various social, economical and human activities. This paper presents a review of the challenges of machine learning with big data. Consequently, synthesizing big data frameworks and deep learning is provided. Different types of data sets and perfect data strategy is described. Also the growth of Big data and number of tactics that can be used when dealing with very large data files for machine learning is explained.},
      keywords = {Big data, machine learning, deep learning, Synthesizing, Artificial Intelligence},
      month = {July},
  }