Jolly Machine Learning with scikit-learn Quick Start Guide

Classification, regression, and clustering techniques in Python
1. Auflage 2018
ISBN: 978-1-78934-737-1
Verlag: De Gruyter
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 172 Seiten

No detailed description available for "Machine Learning with scikit-learn Quick Start Guide".

Autoren/Hrsg.

Jolly, Kevin

Fachgebiete

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

Inhaltsverzeichnis

Table of Contents - Introducing Machine Learning with scikit-learn
- Predicting categories with K-Nearest Neighbours
- Predicting categories with Logistic Regression
- Predicting categories with Naive Bayes and SVMs
- Predicting numeric outcomes with Linear Regression
- Classification & Regression with Trees
- Clustering data with Unsupervised Machine Learning
- Performance evaluation methods

Über Autor(innen)

Jolly Kevin:
Kevin Jolly is a formally educated data scientist with a master's degree in data science from the prestigious King's College London. Kevin works as a statistical analyst with a digital healthcare start-up, Connido Limited, in London, where he is primarily involved in leading the data science projects that the company undertakes. He has built machine learning pipelines for small and big data, with a focus on scaling such pipelines into production for the products that the company has built. Kevin is also the author of a book titled Hands-On Data Visualization with Bokeh, published by Packt. He is the editor-in-chief of Linear, a weekly online publication on data science software and products.