Python 3 and Feature Engineering - Softcover

Campesato, Oswald

Softcover

ISBN 10: 1683929497 ISBN 13: 9781683929499

Verlag: Mercury Learning and Information, 2023

Inhaltsangabe

This book is designed for data scientists, machine learning practitioners, and anyone with a foundational understanding of Python 3.x. In the evolving field of data science, the ability to manipulate and understand datasets is crucial. The book offers content for mastering these skills using Python 3. The book provides a fast-paced introduction to a wealth of feature engineering concepts, equipping readers with the knowledge needed to transform raw data into meaningful information. Inside, you ll find a detailed exploration of various types of data, methodologies for outlier detection using Scikit-Learn, strategies for robust data cleaning, and the intricacies of data wrangling. The book further explores feature selection, detailing methods for handling imbalanced datasets, and gives a practical overview of feature engineering, including scaling and extraction techniques necessary for different machine learning algorithms. It concludes with a treatment of dimensionality reduction, where you ll navigate through complex concepts like PCA and various reduction techniques, with an emphasis on the powerful Scikit-Learn framework. FEATURES Includes numerous practical examples and partial code blocks that illuminate the path from theory to application Explores everything from data cleaning to the subtleties of feature selection and extraction, covering a wide spectrum of feature engineering topics Offers an appendix on working with the awk command-line utility Features companion files available for downloading with source code, datasets, and figures

Über diesen Titel

Über die Autorin bzw. den Autor

Oswald Campesato specializes in Deep Learning, Python, Data Science, and generative AI. He is the author/co-author of over forty books including Google Gemini for Python, Data Cleaning, and GPT-4 for Developers (all Mercury Learning).

Details zur Ausgabe

• Verlag: Mercury Learning and Information
• Erscheinungsdatum: 2023
• ISBN 10: 1683929497
• ISBN 13: 9781683929499
• Einband: Tapa blanda
• Sprache: Englisch
• Auflage: 1
• Anzahl der Seiten: 232
• Kontakt zum Hersteller: Nicht verfügbar