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Spark in Action by Petar Zečević Marko Bonaći

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Chapter 7. Getting smart with MLlib

This chapter covers

  • Machine-learning basics
  • Performing linear algebra in Spark
  • Scaling and normalizing features
  • Training and applying a linear regression model
  • Evaluating the model’s performance
  • Using regularization
  • Optimizing linear regression

Machine learning is a scientific discipline that studies the use and development of algorithms that make computers accomplish complicated tasks without explicitly programming them. That is, the algorithms eventually learn how they can solve a given task. These algorithms include methods and techniques from statistics, probability, and information theory.

Today, machine learning is ubiquitous. Examples include online stores that offer you similar items that ...

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