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Spark for Data Science

You're reading from   Spark for Data Science Analyze your data and delve deep into the world of machine learning with the latest Spark version, 2.0

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Product type Paperback
Published in Sep 2016
Publisher Packt
ISBN-13 9781785885655
Length 344 pages
Edition 1st Edition
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Authors (2):
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Bikramaditya Singhal Bikramaditya Singhal
Author Profile Icon Bikramaditya Singhal
Bikramaditya Singhal
Srinivas Duvvuri Srinivas Duvvuri
Author Profile Icon Srinivas Duvvuri
Srinivas Duvvuri
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Toc

Table of Contents (12) Chapters Close

Preface 1. Big Data and Data Science – An Introduction FREE CHAPTER 2. The Spark Programming Model 3. Introduction to DataFrames 4. Unified Data Access 5. Data Analysis on Spark 6. Machine Learning 7. Extending Spark with SparkR 8. Analyzing Unstructured Data 9. Visualizing Big Data 10. Putting It All Together 11. Building Data Science Applications

Introduction


Machine learning is all about learning by example data; examples that produce a particular output for a given input. There are various business use cases for machine learning. Let us look at a few examples to get an idea of what exactly it is:

  • A recommendation engine that recommends users what they might be interested in buying

  • Customer segmentation (grouping customers who share similar characteristics) for marketing campaigns

  • Disease classification for cancer - malignant/benign

  • Predictive modeling, for example, sales forecasting, weather forecasting

  • Drawing business inferences, for example, understanding what effect will change the price of a product have on sales

The evolution

The concept of statistical learning was existent even before the first computer system was introduced. In the nineteenth century, the least squares technique (now called linear regression) had already been developed. For classification problems, Fisher came up with Linear Discriminant Analysis (LDA). Around...

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