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Python Data Analysis

You're reading from   Python Data Analysis Learn how to apply powerful data analysis techniques with popular open source Python modules

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Product type Paperback
Published in Oct 2014
Publisher
ISBN-13 9781783553358
Length 348 pages
Edition 1st Edition
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Author (1):
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Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
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Toc

Table of Contents (17) Chapters Close

Preface 1. Getting Started with Python Libraries FREE CHAPTER 2. NumPy Arrays 3. Statistics and Linear Algebra 4. pandas Primer 5. Retrieving, Processing, and Storing Data 6. Data Visualization 7. Signal Processing and Time Series 8. Working with Databases 9. Analyzing Textual Data and Social Media 10. Predictive Analytics and Machine Learning 11. Environments Outside the Python Ecosystem and Cloud Computing 12. Performance Tuning, Profiling, and Concurrency A. Key Concepts
B. Useful Functions C. Online Resources
Index

Chapter 10. Predictive Analytics and Machine Learning

Predictive analytics and machine learning are hot, new research fields. They are new compared to other fields and, without a doubt, we can expect a lot of rapid growth. It is even predicted that machine learning will accelerate so fast that within mere decades human labor will be replaced by intelligent machines (see http://en.wikipedia.org/wiki/Technological_singularity). The current state of art is far from that utopia. A lot of computing power and data is still needed to make even simple decisions, such as determining whether pictures on the Internet contain dogs or cats. Predictive analytics uses a variety of techniques, including machine learning to make useful predictions, for instance, to determine whether a customer can repay his or her loans or identify female customers who are pregnant (see http://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/).

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