Python 3 and Feature Engineering
Seiten
2023
Mercury Learning & Information (Verlag)
978-1-68392-949-9 (ISBN)
Mercury Learning & Information (Verlag)
978-1-68392-949-9 (ISBN)
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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 alongside a fast-paced introduction to a wealth of feature engineering concepts, equipping readers with the knowledge needed to transform raw data into meaningful information.
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.
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.
Oswald Campesato (San Francisco, CA) specializes in Deep Learning, Python, and GPT-4. He is the author/co-author of over thirty-five 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
Index
| Erscheinungsdatum | 10.01.2024 |
|---|---|
| Sprache | englisch |
| Themenwelt | Informatik ► Netzwerke ► Sicherheit / Firewall |
| Mathematik / Informatik ► Informatik ► Office Programme | |
| Mathematik / Informatik ► Informatik ► Software Entwicklung | |
| ISBN-10 | 1-68392-949-7 / 1683929497 |
| ISBN-13 | 978-1-68392-949-9 / 9781683929499 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
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