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Learning Data Mining with Python

You're reading from   Learning Data Mining with Python Harness the power of Python to analyze data and create insightful predictive models

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Product type Paperback
Published in Jul 2015
Publisher Packt
ISBN-13 9781784396053
Length 344 pages
Edition 1st Edition
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Author (1):
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Robert Layton Robert Layton
Author Profile Icon Robert Layton
Robert Layton
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Table of Contents (15) Chapters Close

Preface 1. Getting Started with Data Mining FREE CHAPTER 2. Classifying with scikit-learn Estimators 3. Predicting Sports Winners with Decision Trees 4. Recommending Movies Using Affinity Analysis 5. Extracting Features with Transformers 6. Social Media Insight Using Naive Bayes 7. Discovering Accounts to Follow Using Graph Mining 8. Beating CAPTCHAs with Neural Networks 9. Authorship Attribution 10. Clustering News Articles 11. Classifying Objects in Images Using Deep Learning 12. Working with Big Data A. Next Steps… Index

Big data

What makes big data different? Most big-data proponents talk about the four Vs of big data:

  1. Volume: The amount of data that we generate and store is growing at an increasing rate, and predictions of the future generally only suggest further increases. Today's multi-gigabyte sized hard drives will turn into exabyte hard drives in a few years, and network throughput traffic will be increasing as well. The signal to noise ratio can be quite difficult, with important data being lost in the mountain of non-important data.
  2. Velocity: While related to volume, the velocity of data is increasing too. Modern cars have hundreds of sensors that stream data into their computers, and the information from these sensors needs to be analyzed at a subsecond level to operate the car. It isn't just a case of finding answers in the volume of data; those answers often need to come quickly.
  3. Variety: Nice datasets with clearly defined columns are only a small part of the dataset that we have these...
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