Python 3 and Feature Engineering
by 作者: Oswald Campesato (Author)
Publisher Finelybook 出版社: Mercury Learning and Information
Publication Date 出版日期: 2023-12-13
Language 语言: English
pages 页数: : 216 pages
ISBN-10 书号: 1683929497
ISBN-13 书号: 9781683929499
Book Description
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
About the Author
Oswald Campesato specializes in Deep Learning, Python, and GPT-4. He is the author/co-author of over forty books including Python 3 Using ChatGPT / GPT-4, NLP for Developers, and Artificial Intelligence, Machine Learning and Deep Learning (all Mercury Learning).
1: Working with Datasets
2: Outlier and Anomaly Detection
3: Data Cleaning Tasks
4: Data Wrangling
5: Feature Selection
6: Feature Engineering
7: Dimensionality Reduction
Appendix: Working with awk
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