Feature Engineering for Modern Machine Learning with Scikit-Learn: Mastering data preparation and transformation for robust ML models
1146880816
Feature Engineering for Modern Machine Learning with Scikit-Learn: Mastering data preparation and transformation for robust ML models
44.99 In Stock
Feature Engineering for Modern Machine Learning with Scikit-Learn: Mastering data preparation and transformation for robust ML models

Feature Engineering for Modern Machine Learning with Scikit-Learn: Mastering data preparation and transformation for robust ML models

by Cuantum Technologies LLC
Feature Engineering for Modern Machine Learning with Scikit-Learn: Mastering data preparation and transformation for robust ML models

Feature Engineering for Modern Machine Learning with Scikit-Learn: Mastering data preparation and transformation for robust ML models

by Cuantum Technologies LLC

eBook

$44.99 

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Product Details

ISBN-13: 9781837026708
Publisher: Packt Publishing
Publication date: 01/23/2025
Sold by: Barnes & Noble
Format: eBook
Pages: 436
File size: 2 MB

About the Author

Cuantum Technologies is a leading innovator in the realm of software development and education, with a special focus on leveraging the power of Artificial Intelligence and cutting-edge technology. They specialize in web-based software development, authoring insightful programming and AI literature, and building captivating web experiences with the intricate use of HTML, CSS, JavaScript, and Three.js. Their diverse array of products includes CuantumAI, a pioneering SaaS offering, and an array of books spanning from Python, NLP, PHP, JavaScript, and beyond. Through their services, they are constantly striving to demystify AI and technology, making it accessible, understandable, and useable for all.

Table of Contents

Table of Contents
  1. Real-World Data Analysis Projects
  2. Feature Engineering for Predictive Models
  3. Automating Feature Engineering with Pipelines
  4. Feature Engineering for Model Improvement
  5. Advanced Model Evaluation Techniques
  6. Introduction to Feature Selection with Lasso and Ridge
  7. Feature Engineering for Deep Learning
  8. AutoML and Automated Feature Engineering
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