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Machine Learning with Spark and Python : Essential Techniques for Predictive Analytics / Michael Bowles.

By: Material type: TextPublication details: Newark : Wiley, 2019.Edition: 2nd edDescription: 1 online resource (371 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781119562023
  • 1119562023
  • 1119562015
  • 9781119562016
  • 9781119561958
  • 1119561957
Subject(s): Additional physical formats: Print version:: Machine Learning with Spark and Python : Essential Techniques for Predictive Analytics.DDC classification:
  • 006.3/1 23
LOC classification:
  • Q325.5
Online resources:
Contents:
Front Matter -- The Two Essential Algorithms for Making Predictions -- Understand the Problem by Understanding the Data -- Predictive Model Building -- Penalized Linear Regression -- Building Predictive Models Using Penalized Linear Methods -- Ensemble Methods -- Building Ensemble Models with Python.
Summary: Machine Learning with Spark and Python Essential Techniques for Predictive Analytics, Second Edition simplifies ML for practical uses by focusing on two key algorithms. This new second edition improves with the addition of Spark-a ML framework from the Apache foundation. By implementing Spark, machine learning students can easily process much large data sets and call the spark algorithms using ordinary Python code.
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Front Matter -- The Two Essential Algorithms for Making Predictions -- Understand the Problem by Understanding the Data -- Predictive Model Building -- Penalized Linear Regression -- Building Predictive Models Using Penalized Linear Methods -- Ensemble Methods -- Building Ensemble Models with Python.

Print version record.

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Machine Learning with Spark and Python Essential Techniques for Predictive Analytics, Second Edition simplifies ML for practical uses by focusing on two key algorithms. This new second edition improves with the addition of Spark-a ML framework from the Apache foundation. By implementing Spark, machine learning students can easily process much large data sets and call the spark algorithms using ordinary Python code.

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