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Machine Learning at Scale

  • Zubair Nabi
Chapter

Abstract

Data by itself is a static, lifeless entity. You need analytics to breathe life into it and make it talk or even sing. The most sophisticated and popular class of such analytics revolves around nowcasting, forecasting, and recommendations, more generally known as machine learning and data mining. Machine-learning algorithms learn patterns in data and can then be used to make predictions, whereas data mining helps extract structure from unstructured data. Machine learning at scale is the key to practical predictions and recommendations, which are essential to drive the needs of consumers: commercial, academic, or scientific. This chapters uses MLlib to enable such applications.

Keywords

Feature Selection Mean Square Error Recommendation System Inertial Measurement Unit Frequent Itemsets 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Copyright information

© Zubair Nabi 2016

Authors and Affiliations

  • Zubair Nabi
    • 1
  1. 1.LahorePakistan

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