AI for Suspicious Activity Monitoring
AI for Suspicious Activity Monitoring
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 2h 35m | 1.39 GB
Instructor: Minerva Singh
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 2h 35m | 1.39 GB
Instructor: Minerva Singh
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 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 applications
- Step-by-step project-based learning guided by an instructor with an MPhil from the University of Oxford and a PhD from the University of Cambridge
- A rare combination of AI, deep learning, and GenAI in a single course
- Use of cutting-edge LLM frameworks like OpenAI, LangChain, and LLAMA-Index to expand beyond numerical anomaly detection into text-based threat detection
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
AI for Suspicious Activity Monitoring
