M01
What AI is and why it matters now
AI is a way of learning patterns from data so a system can make predictions, rank options, or automate decisions.
Course summary
AI
Use this page to revisit what each stage gives you and return to the exact weak point that needs another pass.
Stage 1 of 3
AI Foundations
Start from data, simple models, and how to read accuracy and bias without drowning in maths.
M02
Data and representation
In AI, the word data sounds fancy, but it is usually boring.
M03
Supervised and unsupervised learning
When we say a model learns, we mean it changes its internal settings so it can make better guesses.
M04
Responsible AI basics and limitations
AI systems can cause harm even when everybody is trying to do the right thing.
Stage 2 of 3
AI Intermediate
Work with evaluation, features, embeddings, and practical model use without the buzzword fog.
M01
Models, parameters and training dynamics
A model is still a function that turns input into output.
M02
Data, features and representation
Raw data is rarely ready for a model.
M03
Evaluation, metrics and failure analysis
Accuracy is an easy number to like because it feels clean.
M04
Deployment, monitoring and drift
Deployment is where good models go to die.
M05
Responsible AI, limits and deployment risks
AI systems do not understand intent or truth.
Stage 3 of 3
AI Advanced
Transformers, agents, diffusion models, and how real AI systems are designed, governed, and sometimes misused.
M01
AI systems and model architectures
A model is a component that maps inputs to outputs.
M02
Scaling, cost and reliability in AI systems
Scaling is not a single knob.
M03
Evaluation, monitoring and governance in production AI
Evaluation in production is not a single score.