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Apache Spark 2.x Cookbook

You're reading from   Apache Spark 2.x Cookbook Over 70 cloud-ready recipes for distributed Big Data processing and analytics

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Product type Paperback
Published in May 2017
Publisher
ISBN-13 9781787127265
Length 294 pages
Edition 1st Edition
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Concepts
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Author (1):
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Rishi Yadav Rishi Yadav
Author Profile Icon Rishi Yadav
Rishi Yadav
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Toc

Table of Contents (13) Chapters Close

Preface 1. Getting Started with Apache Spark FREE CHAPTER 2. Developing Applications with Spark 3. Spark SQL 4. Working with External Data Sources 5. Spark Streaming 6. Getting Started with Machine Learning 7. Supervised Learning with MLlib — Regression 8. Supervised Learning with MLlib — Classification 9. Unsupervised Learning 10. Recommendations Using Collaborative Filtering 11. Graph Processing Using GraphX and GraphFrames 12. Optimizations and Performance Tuning

Introduction


The following is Wikipedia's definition of machine learning:

"Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data."

Essentially, machine learning is the process of using past data to make predictions about the future. Machine learning heavily depends upon statistical analysis and methodology.

In statistics, there are four types of measurement scales:

Scale type

Description

Nominal scale

  • =, ≠
  • Identifies categories
  • Can't be numeric
  • Example: male, female

Ordinal scale

  • =, ≠, <, >
  • Nominal scale +
  • Ranks from least important to most important
  • Example: corporate hierarchy

Interval scale

  • =, ≠, <, >, +, -
  • Ordinal scale + distance between observations
  • Numbers assigned to observations indicate order
  • Difference between any consecutive values is same as others
  • 60° temperature is not doubly hot than 30°

Ratio scale

  • =, ≠, <, >, +, ×, ÷
  • Interval scale +ratios of observations
  • $20 is twice as costly as $10

Another distinction that...

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