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You're reading from  Python Data Mining Quick Start Guide

Product typeBook
Published inApr 2019
Reading LevelBeginner
PublisherPackt
ISBN-139781789800265
Edition1st Edition
Languages
Python
Concepts
Data Mining
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Author (1)
Nathan Greeneltch
Nathan Greeneltch
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Nathan Greeneltch

Nathan Greeneltch, PhD is a ML engineer at Intel Corp and resident data mining and analytics expert in the AI consulting group. Hes worked with Python analytics in both the start-up realm and the large-scale manufacturing sector over the course of the last decade. Nathan regularly mentors new hires and engineers fresh to the field of analytics, with impromptu chalk talks and division-wide knowledge-sharing sessions at Intel. In his past life, he was a physical chemist studying surface enhancement of the vibration signals of small molecules; a topic on which he wrote a doctoral thesis while at Northwestern University in Evanston, IL. Nathan hails from the southeastern United States, with family in equal parts from Arkansas and Florida
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Basic data terminology

This section is meant to be a quick overview of the terms that you should know before you get started. This list is very streamlined and is not exhaustive. Please refer to the suggested reading in Chapter 1, Data Mining and Getting Started with Python Tools, for wider coverage of domain-specific terminology.

Sample spaces

The sample space is the space that is covered by all the possible outcomes of a measurement. For example, if a feature column in a dataset is populated with the number of days last month that a responder watched television, then the sample space will include all the integers in the {0,1,2...31} set. If a manufacturing tool measures the temperature difference before and after processing...

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Python Data Mining Quick Start Guide
Published in: Apr 2019Publisher: PacktISBN-13: 9781789800265
© 2019 Packt Publishing Limited All Rights Reserved

Author (1)

author image
Nathan Greeneltch

Nathan Greeneltch, PhD is a ML engineer at Intel Corp and resident data mining and analytics expert in the AI consulting group. Hes worked with Python analytics in both the start-up realm and the large-scale manufacturing sector over the course of the last decade. Nathan regularly mentors new hires and engineers fresh to the field of analytics, with impromptu chalk talks and division-wide knowledge-sharing sessions at Intel. In his past life, he was a physical chemist studying surface enhancement of the vibration signals of small molecules; a topic on which he wrote a doctoral thesis while at Northwestern University in Evanston, IL. Nathan hails from the southeastern United States, with family in equal parts from Arkansas and Florida
Read more about Nathan Greeneltch

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