Machine Learning with Apache Spark Quick Start Guide

Machine Learning with Apache Spark Quick Start Guide

eBook Details:

  • Paperback: 240 pages
  • Publisher: WOW! eBook (December 26, 2018)
  • Language: English
  • ISBN-10: 1789346568
  • ISBN-13: 978-1789346565

eBook Description:

Machine Learning with Apache Spark Quick Start Guide: Combine advanced analytics including Machine Learning, Deep Learning Neural Networks and Natural Language Processing with modern scalable technologies including Apache Spark to derive actionable insights from Big Data in real-time

Every person and every organization in the world manages data, whether they realize it or not. Data is used to describe the world around us and can be used for almost any purpose, from analyzing consumer habits to fighting disease and serious organized crime. Ultimately, we manage data in order to derive value from it, and many organizations around the world have traditionally invested in technology to help process their data faster and more efficiently.

But we now live in an interconnected world driven by mass data creation and consumption where data is no longer rows and columns restricted to a spreadsheet, but an organic and evolving asset in its own right. With this realization comes major challenges for organizations: how do we manage the sheer size of data being created every second (think not only spreadsheets and databases, but also social media posts, images, videos, music, blogs and so on)? And once we can manage all of this data, how do we derive real value from it?

The focus of Machine Learning with Apache Spark Quick Start Guide is to help us answer these questions in a hands-on manner. We introduce the latest scalable technologies to help us manage and process big data. We then introduce advanced analytical algorithms applied to real-world use cases in order to uncover patterns, derive actionable insights, and learn from this big data.

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1 Response

  1. June 30, 2019

    […] Machine Learning with R Quick Start Guide: Learn how to use R to apply powerful machine learning methods and gain insight into real-world applications using clustering, logistic regressions, random forests, support vector machine, and more […]

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