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Mastering Machine Learning with Spark 2.x

You're reading from   Mastering Machine Learning with Spark 2.x Harness the potential of machine learning, through spark

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Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781785283451
Length 340 pages
Edition 1st Edition
Languages
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Authors (3):
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Alex Tellez Alex Tellez
Author Profile Icon Alex Tellez
Alex Tellez
Michal Malohlava Michal Malohlava
Author Profile Icon Michal Malohlava
Michal Malohlava
Max Pumperla Max Pumperla
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Max Pumperla
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Toc

Summary

In this chapter, we wanted to give you a brief glimpse into the life of a data scientist, what this entails, and some of the challenges that data scientists consistently face. In light of these challenges, we feel that the Apache Spark project is ideally positioned to help tackle these topics, which range from data ingestion and feature extraction/creation to model building and deployment. We intentionally kept this chapter short and light on verbiage because we feel working through examples and different use cases is a better use of time as opposed to speaking abstractly and at length about a given data science topic. Throughout the rest of this book, we will focus solely on this process while giving best-practice tips and recommended reading along the way for users who wish to learn more. Remember that before embarking on your next data science project, be sure to clearly define the problem beforehand, so you can ask an intelligent question of your data and (hopefully) get an intelligent answer!

One awesome website for all things data science is KDnuggets (http://www.kdnuggets.com). Here's a great article on the language all data scientists must learn in order to be successful (http://www.kdnuggets.com/2015/09/one-language-data-scientist-must-master.html).

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