Python 3 and Feature Engineering

Paperback
December 2023
9781683929499
More details
  • Publisher
    Mercury Learning and Information
  • Published
    13th December 2023
  • ISBN 9781683929499
  • Language English
  • Pages 216 pp.
  • Size 6" x 9"
$54.99
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December 2023
9781683929475
More details
  • Publisher
    Mercury Learning and Information
  • Published
    12th December 2023
  • ISBN 9781683929475
  • Language English
  • Pages 216 pp.
  • Size 6" x 9"
$54.99
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December 2023
9781683929482
More details
  • Publisher
    Mercury Learning and Information
  • Published
    12th December 2023
  • ISBN 9781683929482
  • Language English
  • Pages 216 pp.
  • Size 6" x 9"
$155.00

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

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

Oswald Campesato

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).

data science; machine learning; Python; datasets; data wrangling; awk; artificial intelligence