AI for Suspicious Activity Monitoring (Udemy.com)
Build AI-Powered Systems to Detect Anomalies, Fraud, and Unusual Patterns in Real-Time Using Machine Learning & Gen AI
Created by: Minerva Singh
Last updated May 2025
What you will 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
How it compares
Against the other 371 rated Udemy courses in Data Analysis on CourseDuck:
- Rating4.2 out of 5higher than 13% of them
- Reviews213#271 of 372 by number of reviews
- Length2.6 hoursshorter than 73% of them
- Last updatedMay 2025earlier than 54% of them
Course Description
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?
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
Instructor Details
- 4.2 Rating
213 Reviews
Minerva Singh
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
More courses by Minerva Singh
Explainable AI (XAI) For Generative AI (2026)
4.4 (10,448 Reviews)
Provider: Udemy
Time: 2.8h
$19.99
Core Spatial Data Analysis: Introductory GIS with R and QGIS (2025)
4.2 (2,392 Reviews)
Provider: Udemy
Time: 2.7h
$13.99
ArcGIS Desktop For Spatial Analysis: Go From Basic To Pro (2025)
4.6 (2,253 Reviews)
Provider: Udemy
Time: 5h
$94.99
Introduction to Large Language Models (LLMs) In Python (2025)
4.5 (2,212 Reviews)
Provider: Udemy
Time: 2.9h
$19.99
Complete Data Science Training with Python for Data Analysis (2025)
4.3 (2,204 Reviews)
Provider: Udemy
Time: 13.1h
$12.99
Applied Statistical Modeling for Data Analysis in R (2025)
4.4 (2,049 Reviews)
Provider: Udemy
Time: 10h
$99.99
More Data Analysis courses
Complete neural signal processing and analysis: Zero to hero (2026)
4.8 (2,013 Reviews)
Provider: Udemy
Time: 46.9h
$12.99
Excel for Business Analysts Online Course (2023)
4.8 (636 Reviews)
Provider: Udemy
Time: 9.3h
$74.99
Alteryx Designer Advanced Certification Mega Bundle (2023)
4.8 (468 Reviews)
Provider: Udemy
Time: 16.9h
$69.99
Reviews
By Sathish Kumar D on 6/2/2026
There is nothing related to the topic suspicious activity monitoring. A waste of time for people from security. This is completely over AI and LLM and not related to cybersecurity or anomaly monitoring.
By ANTONIO CHAGAS on 10/6/2025
could be more dynamic, with more presentations, explanations, showing the code has resources for download
By Aharez Imad on 8/31/2025
Before this course, I had little knowledge about AI in security, but now I can confidently say I understand the fundamentals of machine learning and how it helps in monitoring.
By Olivia J. White on 8/13/2025
If you're interested in how AI is revolutionizing the banking world, particularly in spotting fraud and tracking transactions, this course offers incredible insights you won’t want to miss.
By Minh Bùi on 8/13/2025
This course expertly breaks down practical examples—like AI-powered fraud prevention and complex trading situations—using detailed case studies that make the learning engaging and impactful.
By Anthony Tendencia on 8/12/2025
The course offered straightforward, actionable guidance with a well-organized flow, spanning from fundamental AI concepts to regulatory compliance, along with insightful technical practice.
By Anonymized User on 7/24/2025
Shows how to build AI for suspicious activity monitoring.
By Sa Mith on 6/20/2025
Each lesson flowed so naturally into the next, and the part on anomaly detection made complex ideas surprisingly easy to understand, even without a technical background.
By Danyial Tariq on 6/20/2025
This course delivered practical, easy-to-apply solutions with a clear structure—covering everything from AI basics to compliance, plus some valuable hands-on technical insights.
By Khairul Anuar Mokhtar on 6/18/2025
This course brilliantly unpacks real-world applications—from AI-driven fraud detection to intricate trade scenarios—with rich case studies that truly elevate the learning experience.
Quality Score
No CourseDuck member has rated this course yet. Taken it? Give each part a thumbs up or down.
Overall Score : 84 / 100












