course-lesson
The Generative AI Revolution
Why tools like ChatGPT, Claude, and image generators suddenly feel different, what tokens and context windows are, how these models are actually built, and the one insight that explains both their magic and their mistakes.
Machine Learning, Deep Learning, and Neural Networks
You keep hearing these three terms. Here is the plain-language difference, what a "parameter" is, how a network learns through many layers, and why deep learning is behind almost every recent breakthrough.
How Machines "Learn": A Plain-English Guide
No math, no code. A friendly but deeper explanation of training data, features, labels, the three main styles of learning, and what really goes on when a machine "learns" something.
A Short History of AI: How We Got Here
AI did not appear overnight. A brief, friendly tour of the decades of ideas, winters, and breakthroughs (Dartmouth, perceptrons, expert systems, Deep Blue, ImageNet, transformers) that led to the tools everyone is talking about today.
What AI Actually Is (and Isn't)
Cut through the hype and the science fiction. A clear, jargon-free definition of artificial intelligence, the different things people mean by the word, how it differs from ordinary software, and what it can really do today.
The Operating Model: Putting It All Together
The final part assembles every thread of the course into a single coherent way of working — how the pieces connect, how to build the capability incrementally, and how to make regulated AI a durable institutional competence rather than a project.
Third-Party and Foundation-Model Risk
Increasingly the model at the heart of your system was built by someone else and is opaque even to you. This part covers governing vendor and foundation models — due diligence, contractual control, and validating what you cannot fully inspect.
Incident Response and Model Failure
AI systems will fail; the question is whether you are ready. This part covers what counts as an AI incident, how to contain and remediate one, the obligations that failure can trigger, and how to learn from it.
Monitoring, Drift, and Continuous Validation
A model that was safe at launch can become unsafe without anything changing in its code. This part covers what to monitor, how drift creeps in, and how monitoring and revalidation keep a system defensible over its whole life.
Deployment, Change Management, and Versioning
The gap between a validated model and a live one is where many failures hide. This part covers deploying safely, ensuring what runs matches what was approved, and controlling the changes that inevitably follow.
Security and Adversarial Robustness
AI systems face attacks that conventional software does not. This part covers the adversarial threat landscape — poisoning, evasion, extraction, inversion, and prompt injection — and how security becomes a governance obligation.
Tooling, Permissions, and Blast-Radius Containment
An agent is only as safe as the permissions behind its tools. This part covers least-privilege design, enforcing boundaries through real access controls rather than instructions, and engineering systems so that a wrong action is survivable.