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 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
Lifetime 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
Instructor
- 4.4 Instructor Rating
- 28,266 Reviews
- 145,358 Students
- 61 Courses
I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics.
I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing and given guest lectures on prestigious forums such as Open Data Science Conference (ODSC).