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Machine Learning for Imbalanced Data

You're reading from Machine Learning for Imbalanced Data Tackle imbalanced datasets using machine learning and deep learning techniques

Product type Paperback

Published in Nov 2023

Publisher Packt

ISBN-13 9781801070836

Length 344 pages

Edition 1st Edition

Languages

Rust

Tools

TensorFlow Lite

Concepts

Data Science

Authors (2):

Dr. Mounir Abdelaziz

Kumar Abhishek

View More author details

Table of Contents (15) Chapters

Preface

1. Chapter 1: Introduction to Data Imbalance in Machine Learning FREE CHAPTER

2. Chapter 2: Oversampling Methods

3. Chapter 3: Undersampling Methods

4. Chapter 4: Ensemble Methods

5. Chapter 5: Cost-Sensitive Learning

6. Chapter 6: Data Imbalance in Deep Learning

7. Chapter 7: Data-Level Deep Learning Methods

8. Chapter 8: Algorithm-Level Deep Learning Techniques

9. Chapter 9: Hybrid Deep Learning Methods

10. Chapter 10: Model Calibration

11. Assessments

12. Index

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13. Other Books You May Enjoy

Appendix: Machine Learning Pipeline in Production

Machine Learning Pipeline in Production

In this appendix, we will look at when and at which step we incorporate the data imbalance-handling techniques within a production machine learning pipeline. This mainly applies to supervised classification problems.

The rest of the chapter is locked

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Authors (2)

Kumar Abhishek

Kumar Abhishek

Kumar Abhishek is a seasoned Senior Machine Learning Engineer at Expedia Group, US, specializing in risk analysis and fraud detection for Expedia brands. With over a decade of experience at companies such as Microsoft, Amazon, and a Bay Area startup, Kumar holds an MS in Computer Science from the University of Florida.

See other products by Kumar Abhishek

Dr. Mounir Abdelaziz

Dr. Mounir Abdelaziz

Dr. Mounir Abdelaziz is a deep learning researcher specializing in computer vision applications. He holds a Ph.D. in computer science and technology from Central South University, China. During his Ph.D. journey, he developed innovative algorithms to address practical computer vision challenges. He has also authored numerous research articles in the field of few-shot learning for image classification.

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