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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
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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
Read more about Nathan Greeneltch

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Launching a Jupyter Notebook

The Jupyter project spun out of the popular IPython Notebook work of the early 2000s. These notebooks provide a visual interface with sequential text and code cells. This allows you to add some text to describe a solution, then follow it with code examples. The Jupyter Notebook also use the IPython console (similar to Spyder), so you have an interactive code interpretor that can plot images inline. Launching the notebook from the Anaconda prompt is simple:

(base) $ jupyter notebook

The Jupyter project maintains a few basic notebooks. Let's look at a screenshot from one of them, as follows (it can be found at http://nbviewer.jupyter.org/github/temporaer/tutorial_ml_gkbionics):

The concept is self-explanatory if we look at a few examples. The following are recommendations for some relevant and helpful Jupyter Notebooks on data mining and analytics from around the web:

https://github.com/rasbt/python-machine-learning-book/blob/master/code/ch01/ch01.ipynb

http://nbviewer.jupyter.org/github/amplab/datascience-sp14/blob/master/hw2/HW2.ipynb

https://github.com/TomAugspurger/PyDataSeattle/blob/master/notebooks/1.%20Basics.ipynb

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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