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Data Balancing with Gen AI: Credit Card Fraud Detection
  • Level Intermediate
  • Duration 3 hours
  • Course by Coursera Project Network
  • Offered by

About

In this 2-hour guided project, you will learn how to leverage Generative AI for data generation to address data imbalance. SecureTrust Financial Services, a financial institution, has asked us to help them improve the accuracy of their fraud detection system. The model is a binary classifier, but it's not performing well due to data imbalance. As data scientists, we will employ Generative Adversarial Networks (GANs), a subset of Generative AI, to create synthetic fraudulent transactions that closely resemble real transactions. This approach aims to balance the dataset and enhance the accuracy of the fraud detection model. This guided project is designed for those interested in learning how Generative models can increase model accuracy by generating synthetic data. To make the most of this project, it is recommended to have at least one year of experience using deep learning frameworks such as TensorFlow and Keras in Python.

Modules

Your Learning Journey

    • 2 Readings
    • 1 Plugin
    • 2 Lab
    • 1 Assignment
Show info about module content

1 Assignment

  • Assess Your Knowledge

2 Labs

  • Hands-on Project
  • [Optional] Access Your GPT GenAI Playground

2 Readings

  • Project Overview
  • [Optional] The GPT Generative AI Lab Playground

Auto Summary

Boost your fraud detection skills with the "Data Balancing with Gen AI: Credit Card Fraud Detection" course. In just 2 hours, you'll master using Generative Adversarial Networks (GANs) to generate synthetic data and address data imbalances. Ideal for intermediate learners with experience in TensorFlow and Keras, this guided project from Coursera enhances your model accuracy in fraud detection. Enjoy free access and elevate your data science expertise.

Ahmad Varasteh
Instructor

Ahmad Varasteh