You're reading from Machine Learning for Imbalanced Data

Product typeBook

Published inNov 2023

Reading LevelBeginner

PublisherPackt

ISBN-139781801070836

Edition1st Edition

Languages

Rust

Tools

TensorFlow Lite

Concepts

Data Science

Authors (2):

Kumar Abhishek

Dr. Mounir Abdelaziz

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Technical requirements

Similar to prior chapters, we will continue to utilize common libraries such as numpy, scikit-learn, xgboost, and imbalanced-learn. The code and notebooks for this chapter are available on GitHub at https://github.com/PacktPublishing/Machine-Learning-for-Imbalanced-Data/tree/main/chapter05. You can open this GitHub notebook using Google Colab by clicking on the Open in Colab icon at the top of this chapter’s notebook or by launching it from https://colab.research.google.com using the GitHub URL of the notebook.

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Machine Learning for Imbalanced Data

Published in: Nov 2023Publisher: PacktISBN-13: 9781801070836

Authors (2)

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.
Read more about Kumar Abhishek

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.
Read more about Dr. Mounir Abdelaziz

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