Raschka Python Machine Learning

Learn how to build powerful Python machine learning algorithms to generate useful data insights with this data analysis tutorial
1. Auflage 2015
ISBN: 978-1-78355-514-7
Verlag: De Gruyter
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 454 Seiten

No detailed description available for "Python Machine Learning".

Autoren/Hrsg.

Raschka, Sebastian

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Programmier- und Skriptsprachen

Inhaltsverzeichnis

Table of Contents - Giving Computers the Ability to Learn from Data
- Training Machine Learning Algorithms for Classification

- A Tour of Machine Learning Classifiers Using Scikit-Learn
- Building Good Training Sets – Data Pre-Processing
- Compressing Data via Dimensionality Reduction
- Learning Best Practices for Model Evaluation and Hyperparameter Optimization
- Combining Different Models for Ensemble Learning
- Applying Machine Learning To Sentiment Analysis
- Embedding a Machine Learning Model into a Web Application
- Predicting Continuous Target Variables with Regression Analysis
- Working with Unlabeled Data – Clustering Analysis
- Training Artificial Neural Networks for Image Recognition
- Parallelizing Neural Network Training via Theano

Über Autor(innen)

Raschka Sebastian:

Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. As Lead AI Educator at Grid AI, Sebastian plans to continue following his passion for helping people get into machine learning and artificial intelligence.