Study guide
Artificial Intelligence study guide
Artificial intelligence is changing how people work, learn, create, communicate, and solve problems. This structured learning path takes an absolute beginner toward confident practical understanding.
- Modules
- 7 modules
- Lessons
- 50 lessons
- Estimated time
- ~35-50 hours
- Key terms
- 94 key terms
How to work through it
- 1. Follow the order below. The modules build on each other — start at the top and work down.
- 2. Recall before you re-read. After each lesson, try to explain the idea from memory, then check yourself with the flashcards.
- 3. Test at the end of each module. The quizzes below show what stuck and what to review.
Full lesson outline
Module 1. AI Foundations
Module 2. Machine Learning
Module 3. Generative AI
Module 4. Deep Learning
Module 5. AI Applications
Module 6. AI Ethics and Society
Practise & review
- Glossary94 terms defined in plain language
- FlashcardsDrill the vocabulary with active recall
- AI Foundations quiz10 questions, graded instantly
- Machine Learning quiz14 questions, graded instantly
- Generative AI quiz14 questions, graded instantly
- Deep Learning quiz14 questions, graded instantly
- AI Applications quiz14 questions, graded instantly
- AI Ethics and Society quiz14 questions, graded instantly
- Building with AI quiz14 questions, graded instantly
Key terms
The vocabulary you’ll meet, each linked to its full definition in the glossary.
- Activation function
- Adversarial example
- AI agent
- AI governance
- AI lifecycle
- AI winter
- Algorithm
- Algorithmic bias
- Alignment problem
- AlphaFold
- API
- API (application programming interface)
- Artificial general intelligence
- Artificial intelligence
- Artificial neuron
- Artificial superintelligence
- Attention
- Augmentation
- Automation
- Autonomous vehicle
- Backpropagation
- Bias
- Classification
- Clinical decision support
- Clustering
- Confusion matrix
- Context
- Context window
- Convolutional neural network
- Data
- Dataset
- Deep learning
- Deepfake
- Diffusion model
- Discriminative model
- Distribution shift
- Embedding
- Evaluation
- Expert system
- Facial recognition
- Fairness (in AI)
- Feature
- Fine-tuning
- Foundation model
- Fraud detection
- Generalization
- Generative AI
- Gradient descent
- Guardrails
- Hallucination
- Hidden layer
- Human-in-the-loop
- Inference
- Intelligent tutoring system
- Label
- Large language model
- Levels of driving automation
- Liar's dividend
- Limited-memory system
- Loss function
- Machine learning
- Minimum viable product (MVP)
- Model
- Multimodal model
- Narrow AI
- Neural network
- No-code / low-code
- Open-weight model
- Overfitting
- Overreliance
- Parameter
- Precision
- Prompt
- Prompt engineering
- Reactive system
- Recall
- Recommendation system
- Red-teaming
- Regression
- Reinforcement learning
- Responsible AI
- Retrieval-augmented generation (RAG)
- Reward hacking
- Supervised learning
- Surveillance
- Synthetic media
- Text-to-image
- Token
- Tool use / function calling
- Training
- Transformer
- Underfitting
- Unsupervised learning
- Weight