Machine Learning for Imbalanced Data

Machine Learning for Imbalanced Data explores the challenges and solutions for dealing with datasets where certain classes dominate over others. By understanding and applying techniques such as over-sampling, under-sampling, and cost-sensitive training using modern machine learning frameworks like PyTorch, you will gain the tools to create performant models even in imbalanced scenarios.

What this Book will help me do

• Learn efficient sampling strategies like over-sampling and under-sampling to handle imbalanced data.
• Gain understanding of algorithm-based methods like cost-sensitive learning and threshold tuning to improve minority class prediction.
• Master deep learning techniques tailored for imbalanced datasets including adjustments at the data and algorithm levels.
• Develop the skills to properly evaluate models with specialized metrics suitable for imbalanced datasets.
• Pragmatically implement state-of-the-art techniques in Python and integrate them into real-world machine learning pipelines.

Author(s)

Kumar Abhishek is a seasoned data scientist with extensive experience in applying machine learning to complex datasets. Dr. Mounir Abdelaziz, a researcher in artificial intelligence, has a PhD in deep learning and a proven record of academic and industry contributions. Together, they blend hands-on practice with scientific rigor in an accessible writing style.

Who is it for?

This book is tailored for machine learning practitioners looking to address challenges in imbalanced datasets, including professionals like data scientists, ML engineers, and research scientists. Ideal readers should have a basic understanding of machine learning concepts and seek to deepen their expertise in this specific area. Beginners are welcome to explore the initial chapters to gain foundational insights.