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Feature Engineering for Modern Machine Learning with Scikit-Learn
Feature Engineering for Modern Machine Learning with Scikit-Learn

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

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Profile Icon Cuantum Technologies LLC
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£29.99 £33.99
eBook Jan 2025 436 pages 1st Edition
eBook
£29.99 £33.99
Arrow left icon
Profile Icon Cuantum Technologies LLC
Arrow right icon
£29.99 £33.99
eBook Jan 2025 436 pages 1st Edition
eBook
£29.99 £33.99
eBook
£29.99 £33.99

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Key benefits

  • Comprehensive guide to feature engineering for Scikit-Learn
  • Hands-on projects for real-world applications
  • Focus on automation, pipelines, and deep learning integration

Description

Feature engineering is essential for building robust predictive models. This book delves into practical techniques for transforming raw data into powerful features using Scikit-Learn. You'll explore automation, deep learning integrations, and advanced topics like feature selection and model evaluation. Learn to handle real-world data challenges, enhance accuracy, and streamline your workflows. Through hands-on projects, readers will gain practical experience with techniques such as clustering, pipelines, and feature selection, applied to domains like retail and healthcare. Step-by-step instructions ensure a comprehensive learning journey, from foundational concepts to advanced automation and hybrid modeling approaches. By combining theory with real-world applications, the book equips data professionals with the tools to unlock the full potential of machine learning models. Whether working with structured datasets or integrating deep learning features, this guide provides actionable insights to tackle any data transformation challenge effectively.

Who is this book for?

Data scientists, machine learning engineers, and analytics professionals looking to improve predictive model performance will find this book invaluable. Prior experience with Python and basic machine learning concepts is recommended. Familiarity with Scikit-Learn is helpful but not required.

What you will learn

  • Create data-driven features for better ML models
  • Apply Scikit-Learn pipelines for automation
  • Use clustering and feature selection effectively
  • Handle imbalanced datasets with advanced techniques
  • Leverage regularization for feature selection
  • Utilize deep learning for feature extraction

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jan 23, 2025
Length: 436 pages
Edition : 1st
Language : English
ISBN-13 : 9781837026708
Category :
Languages :

What do you get with eBook?

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OR
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Product Details

Publication date : Jan 23, 2025
Length: 436 pages
Edition : 1st
Language : English
ISBN-13 : 9781837026708
Category :
Languages :

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Table of Contents

76 Chapters
Who we are Chevron down icon Chevron up icon
Our Philosophy: Chevron down icon Chevron up icon
Our Expertise: Chevron down icon Chevron up icon
Code Blocks Resource Chevron down icon Chevron up icon
Premium Customer Support Chevron down icon Chevron up icon
TABLE OF CONTENTS Chevron down icon Chevron up icon
Introduction Chevron down icon Chevron up icon
Part 1: Practical Applications and Case Studies Chevron down icon Chevron up icon
Chapter 1: Real-World Data Analysis Projects Chevron down icon Chevron up icon
1.1 End-to-End Data Analysis: Healthcare Data Chevron down icon Chevron up icon
1.2 Case Study: Retail Data and Customer Segmentation Chevron down icon Chevron up icon
1.3 Practical Exercises for Chapter 1 Chevron down icon Chevron up icon
1.4 What Could Go Wrong? Chevron down icon Chevron up icon
Chapter 1 Summary Chevron down icon Chevron up icon
Chapter 2: Feature Engineering for Predictive Models Chevron down icon Chevron up icon
2.1 Predicting Customer Churn: Healthcare Data Chevron down icon Chevron up icon
2.2 Feature Engineering for Classification and Regression Models Chevron down icon Chevron up icon
2.3 Practical Exercises for Chapter 2 Chevron down icon Chevron up icon
2.4 What Could Go Wrong? Chevron down icon Chevron up icon
Chapter 2 Summary Chevron down icon Chevron up icon
Quiz Part 1: Practical Applications and Case Studies Chevron down icon Chevron up icon
Answers Chevron down icon Chevron up icon
Project 1: Customer Segmentation using Clustering Techniques Chevron down icon Chevron up icon
1. Understanding the K-means Clustering Algorithm Chevron down icon Chevron up icon
2. Advanced Clustering Techniques Chevron down icon Chevron up icon
3. Evaluating Clustering Results Chevron down icon Chevron up icon
Part 2: Integration with Scikit-Learn for Model Building Chevron down icon Chevron up icon
Chapter 3: Automating Feature Engineering with Pipelines Chevron down icon Chevron up icon
3.1 Pipelines in Scikit-learn: A Deep Dive Chevron down icon Chevron up icon
3.2 Automating Data Preprocessing with FeatureUnion Chevron down icon Chevron up icon
3.3 Practical Exercises for Chapter 3 Chevron down icon Chevron up icon
3.4 What Could Go Wrong? Chevron down icon Chevron up icon
Chapter 3 Summary Chevron down icon Chevron up icon
Chapter 4: Feature Engineering for Model Improvement Chevron down icon Chevron up icon
4.1 Using Feature Importance to Guide Engineering Chevron down icon Chevron up icon
4.2 Recursive Feature Elimination (RFE) and Model Tuning Chevron down icon Chevron up icon
4.3 Practical Exercises for Chapter 4 Chevron down icon Chevron up icon
4.4 What Could Go Wrong? Chevron down icon Chevron up icon
Chapter 4 Summary Chevron down icon Chevron up icon
Chapter 5: Advanced Model Evaluation Techniques Chevron down icon Chevron up icon
5.1 Cross-Validation Revisited: Stratified, Time-Series Chevron down icon Chevron up icon
5.2 Dealing with Imbalanced Data: SMOTE, Class Weighting Chevron down icon Chevron up icon
5.3 Practical Exercises for Chapter 5 Chevron down icon Chevron up icon
5.4 What Could Go Wrong? Chevron down icon Chevron up icon
Chapter 5 Summary Chevron down icon Chevron up icon
Quiz Part 2: Integration with Scikit-Learn for Model Building Chevron down icon Chevron up icon
Answers Chevron down icon Chevron up icon
Part 3: Advanced Topics and Future Trends Chevron down icon Chevron up icon
Project 2: Feature Engineering with Deep Learning Models Chevron down icon Chevron up icon
1.1 Leveraging Pretrained Models for Feature Extraction Chevron down icon Chevron up icon
1.2 Integrating Deep Learning Features with Traditional Machine Learning Models Chevron down icon Chevron up icon
1.3 Fine-Tuning Pretrained Models for Enhanced Feature Learning Chevron down icon Chevron up icon
1.4 End-to-End Feature Learning in Hybrid Architectures Chevron down icon Chevron up icon
1.5 Deployment Strategies for Hybrid Deep Learning Models Chevron down icon Chevron up icon
Chapter 6: Introduction to Feature Selection with Lasso and Ridge Chevron down icon Chevron up icon
6.1 Regularization Techniques for Feature Selection Chevron down icon Chevron up icon
6.2 Hyperparameter Tuning for Feature Engineering Chevron down icon Chevron up icon
6.3 Practical Exercises: Chapter 6 Chevron down icon Chevron up icon
6.4 What Could Go Wrong? Chevron down icon Chevron up icon
Chapter 6 Summary Chevron down icon Chevron up icon
Chapter 7: Feature Engineering for Deep Learning Chevron down icon Chevron up icon
7.1 Preparing Data for Neural Networks Chevron down icon Chevron up icon
7.2 Integrating Feature Engineering with TensorFlow/Keras Chevron down icon Chevron up icon
7.3 Practical Exercises: Chapter 7 Chevron down icon Chevron up icon
7.4 What Could Go Wrong? Chevron down icon Chevron up icon
Chapter 7 Summary Chevron down icon Chevron up icon
Chapter 8: AutoML and Automated Feature Engineering Chevron down icon Chevron up icon
8.1 Exploring Automated Feature Engineering Tools Chevron down icon Chevron up icon
8.2 Introduction to Feature Tools and AutoML Libraries Chevron down icon Chevron up icon
8.3 Practical Exercises: Chapter 8 Chevron down icon Chevron up icon
8.4 What Could Go Wrong? Chevron down icon Chevron up icon
Chapter 8 Summary Chevron down icon Chevron up icon
Quiz Part 3: Advanced Topics and Future Trends Chevron down icon Chevron up icon
Conclusion Chevron down icon Chevron up icon
Where to continue? Chevron down icon Chevron up icon
Know more about us Chevron down icon Chevron up icon
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