AI for Suspicious Activity Monitoring

About this course

Disclaimer- This course contains the use of artificial intelligence

Unlock the power of AI to detect anomalies, fraud, and suspicious behaviour in digital systems. "AI for Suspicious Activity Monitoring" is a hands-on, end-to-end course designed to teach you how to use traditional AI techniques, deep learning, and generative AI (GenAI) to monitor and respond to unusual patterns in real-world data.

Whether you're a developer, data analyst, or aspiring AI professional, this course provides practical tools and strategies to build intelligent monitoring systems using Python, autoencoders, and large language models (LLMs).

What You’ll Learn Anomaly Detection Techniques: Implement classical and modern methods, including statistical outlier detection, clustering-based approaches, and autoencoders.

Deep Learning for Behaviour Monitoring: Use unsupervised learning (e.g., autoencoders) to detect irregular patterns in time series, text, or sensor data.

GenAI & LLM Integration: Explore how large language models like OpenAI’s GPT and frameworks such as LangChain and LLAMA-Index can assist in monitoring human-generated activity (e.g., suspicious conversations, document scans).

Fraud and Cyber Threat Detection: Apply AI tools to detect threats in finance, cybersecurity, e-commerce, and other high-risk domains.

Cloud-Based Implementation: Build scalable pipelines using tools like Google Colab for real-time or batch monitoring.

Text Analysis for Audit Trails: Perform NLP-based extraction, entity recognition, and text summarisation to flag risky interactions and records.

Why Enrol in This Course?

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