Course Highlights
  • Working with Generative AI, delve into email spam classification models, and explore ethical challenges in the field of Fraud Detection.
Curriculum

27 Topics
Gen AI for Fraud Detection Analytics
Introduction to Generative AI
Understanding Gen AI's part in Fraud Detection
Technological Advancements of Generative AI in Fraud Detection
Overview of the Project
Project Development
Data Collection and Pre-Processing
Setting-up LSTM Model
Setting-Up GAN Model Architecture
Ethical Challenges in Fraud Detection
Regulatory compliance and Privacy protection
Course Summary
Course Overview
How to Use Discussion Forums
Unleashing the Potential of Natural Language Processing (NLP)
Introduction to LSTM- A deatiled Explanation
Introduction to Generative Adversarial Networks- From core principles to diverse application
Unveiling Vital TensorFlow Keras Imports for GAN Development
Real world Application of Fraud Detection using GenAI
Practice Project
End Course Knowledge Check: Module Wrap Up and Assessment
Knowledge Check: Overview of Fraud detection and Generative AI
Knowledge Check: Email Fraud Detection using GAN model
Knowledge Check: Best Practices
How do you envision the integration of generative AI in fraud detection transforming the landscape of fraud prevention?
How can generative AI models like GANs (Generative Adversarial Networks) be effectively utilized to improve the accuracy of email spam classification?
What ethical challenges do you foresee in implementing AI-driven fraud detection systems and how can these challenges be mitigated?

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Gen AI for Fraud Detection Analytics

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