AI for Suspicious Activity Monitoring

Overview

Build AI-Powered Systems to Detect Anomalies, Fraud, and Unusual Patterns in Real-Time Using Machine Learning & Gen AI What you'll learn:

Learn about the uses of self-supervised machine learningImplement self-supervised machine learning frameworks such as autoencoders using PythonLearn about deep learning frameworks such as Keras and H2OLearn about Gen AI and LLM Frameworks 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

Syllabus

• Introduction to AI for Suspicious Activity Monitoring
Overview of Course Objectives
Introduction to Anomaly Detection Systems
Importance of AI in Cybersecurity and Fraud Prevention
• Basics of Anomaly Detection
Statistical Outlier Detection Techniques
Clustering-Based Approaches
Introduction to Self-Supervised Machine Learning
• Self-Supervised Machine Learning
Understanding Autoencoders
Implementing Autoencoders in Python
Case Studies: Anomaly Detection with Autoencoders
• Deep Learning Frameworks
Introduction to Keras for Deep Learning
Exploring H2O for AI Solutions
Practical Exercises using Keras and H2O
• GenAI and Large Language Models (LLMs)
Overview of Generative AI Concepts
Utilizing OpenAI’s GPT for Monitoring
Frameworks: LangChain and LLAMA-Index
Monitoring Human-Generated Activity
• Fraud and Cyber Threat Detection
AI Applications in Finance, Cybersecurity, and E-commerce
Tools for Threat Detection
Building Intelligent Monitoring Systems
• Cloud-Based AI Implementation
Setting Up Scalable AI Pipelines
Using Google Colab for Real-Time Monitoring
Batch Processing Techniques
• Text Analysis for Suspicious Activity
Natural Language Processing (NLP) Techniques
Text Extraction, Entity Recognition, and Summarisation
Automating Audit Trails and Risk Flagging
• Final Projects and Case Studies
Design and Implementation of a Monitoring System
Case Study: Real-World Application in Finance
Case Study: Real-World Application in Cybersecurity
• Course Conclusion
Recap of Key Learnings
Future Trends in AI for Monitoring
Career Paths and Opportunities in AI
• Additional Resources and Support
Lifetime Access to Course Materials and Updates
Instructor Support and Community Interaction
Additional Reading and Practice on AI Topics

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Taught by

Minerva Singh

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Subjects

Data Science