Ai For Suspicious Activity Monitoring
 

What you'll learn
Learn about the uses of self-supervised machine learning
Implement self-supervised machine learning frameworks such as autoencoders using Python
Learn about deep learning frameworks such as Keras and H2O
Learn about Gen AI and LLM Frameworks
Requirements
Basic Python data science concepts
Basic Python syntax
Understanding of the Colab environment
Introduction to the Gen AI Ecosystem
Description
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?In today's fast-paced digital world, AI-powered monitoring systems are essential to detect threats early, reduce risk, and protect operations. This course offers:A practical, Python-based curriculum tailored for real-world applicationsStep-by-step project-based learning guided by an instructor with an MPhil from the University of Oxford and a PhD from the University of CambridgeA rare combination of AI, deep learning, and GenAI in a single courseUse of cutting-edge LLM frameworks like OpenAI, LangChain, and LLAMA-Index to expand beyond numerical anomaly detection into text-based threat detectionLifetime access, updates, and instructor support
Who this course is for
Data Scientists who want to increase their knowledge of self-supervised machine learning
Students of Artificial Intelligence (AI) and Gen AI
Students interested in learning about frameworks such as autoencoders