Greeneltch Python Data Mining Quick Start Guide

A beginner's guide to extracting valuable insights from your data
1. Auflage 2019
ISBN: 978-1-78980-640-3
Verlag: De Gruyter
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 188 Seiten

No detailed description available for "Python Data Mining Quick Start Guide".

Autoren/Hrsg.

Greeneltch, Nathan

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsarchitektur
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsvisualisierung
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenbankdesign & Datenbanktheorie
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse

Inhaltsverzeichnis

Table of Contents - Data Mining and Getting Started with Python Tools
- Basic Terminology and Our End-to-End Example
- Collecting, Exploring, and Visualizing Data
- Cleaning and Readying Data for Analysis
- Grouping and Clustering Data
- Prediction with Regression and Classification
- Advanced Topics - Building a Data Processing Pipeline and Deploying It

Über Autor(innen)

Greeneltch Nathan:

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