Outlier Detection in Python.
This book provides a comprehensive guide to outlier detection using Python, targeting professionals and researchers in data science and machine learning. It covers foundational concepts, techniques, and trends in outlier detection, including statistical methods, machine learning algorithms, and deep...
Gespeichert in:
- 1. Verfasser:
- Format:
- Elektronisch E-Book
- Sprache:
- Englisch
- Veröffentlicht:
-
New York :
Manning Publications Co. LLC,
2025.
- Ausgabe:
- 1st ed.
- Zusammenfassung:
-
This book provides a comprehensive guide to outlier detection using Python, targeting professionals and researchers in data science and machine learning. It covers foundational concepts, techniques, and trends in outlier detection, including statistical methods, machine learning algorithms, and deep learning approaches. The text explores tools like scikit-learn, PyOD, and various libraries, offering practical insights for handling numeric, categorical, and time-series data. The author emphasizes workflow design, data preprocessing, model evaluation, and ensemble methods to enhance detection accuracy. The book is designed to equip readers with the skills to detect anomalies across diverse domains, such as finance, healthcare, network security, and self-driving vehicles. It is suitable for both beginners and experienced practitioners aiming to improve their anomaly detection systems.
- Umfang:
- 1 online resource (497 pages)
- ISBN:
-
9781638356721
1638356726 - Schlagworte:
- Bezugswerke:
-
Parallelausgabe: 9781633436473Parallelausgabe: 1633436470
- Links: