Joshi A. Tackling Imbalanced Data with Python. Advanced Techniques...2024 torrent

Textbook in PDF format

Tackling Imbalanced Data with Python: Advanced Techniques and Real-World Applications for Tackling Class Imbalance is a comprehensive guide designed for data scientists, Machine Learning engineers, and practitioners who face the ubiquitous challenge of imbalanced datasets. This book addresses one of the most critical yet underexplored problems in Machine Learning, where traditional algorithms fail to perform effectively on datasets with skewed class distributions.
The book provides a systematic approach to understanding and solving class imbalance problems, covering everything from fundamental concepts to cutting-edge techniques. Readers will master data-level solutions including SMOTE and advanced synthetic data generation, algorithm-level approaches such as cost-sensitive learning and focal loss, and ensemble methods specifically designed for imbalanced data. The book extensively covers Deep Learning adaptations, computer vision applications, and natural language processing solutions for imbalanced scenarios.
Class imbalance is a fundamental challenge in Machine Learning that profoundly impacts model performance. When a dataset contains significantly more examples of one class than others, algorithms tend to favor the majority class, potentially overlooking critical minority patterns. This section explores the nature of imbalanced data, from basic definitions to complex multi-class scenarios and domain-specific patterns. We’ll examine how to quantify imbalance, distinguish between different severity levels, and understand the various natural and artificial causes that lead to skewed class distributions. By understanding these foundational aspects, data scientists can better select appropriate techniques to address imbalance issues and build more robust, fair models that perform well across all classes.
With 14 comprehensive chapters containing over 100 practical techniques, this book bridges the gap between theoretical understanding and real-world implementation. Each chapter includes detailed Python implementations using popular libraries like Scikit-learn, Imbalanced-learn, PyTorch, and TensorFlow. Industry-specific case studies spanning healthcare, finance, cybersecurity, and manufacturing demonstrate practical applications. The book also addresses production deployment challenges, model monitoring, and emerging topics like federated learning and explainable AI for imbalanced data, making it an essential resource for building robust, production-ready Machine Learning systems.
Understanding the Imbalanced Data Challenge
Measuring Success Beyond Accuracy
Data-Level Solutions: Resampling Strategies
Advanced Synthetic Data Generation
SMOTE-NC for Mixed Data Types
CTGAN for Tabular Data Generation
VAE-Based Synthetic Sample Creation
GAN Architectures for Minority Classes
Physics-Informed Data Augmentation
Large Language Models for Text Augmentation
Algorithm-Level Approaches
Ensemble Methods for Imbalanced Data
Deep Learning for Imbalanced Data
Computer Vision with Imbalanced Classes
Natural Language Processing Solutions
Time Series and Sequential Data