THE CURIOUS BEGINNER
Understand AI
Explore what AI can do, where it falls short, and how to use it thoughtfully.
Concepts · Responsible use · Everyday applicationsExplore AI coursesFREE · HANDS-ON · FRONTIER AI
Learn frontier AI.
Build with it.
Know its limits.
Free, hands-on courses from how transformers work to RAG, evals, agents and MCP. Read the idea, run the code in your browser, and build projects you can show.
Learn at your pace Practise in your browser Linked to primary sources
READ IT. THINK IT. TRY IT.
Your first data insight.
Three days. Three numbers. What is the average?
minutes = [12, 18, 30]
average = sum(minutes) / len(minutes)
print(average)No account needed. Choose an answer to get feedback.
11free courses open now
5guided learning paths
8frontier topics mapped
45glossary terms
THE ROADMAP
From first principles to agents.
One sequence from understanding how models work to shipping tool-using systems. Each stage links to a course when it is open.
- Open now
Introduction to LLMs
Begin with an available introduction to language models.
- Open now
Python & Data for Machine Learning
Prepare with Python, data, and practical programming.
- Open now
Large Language Models: From Prompts to Practice
Learn how to prompt and evaluate language models.
- Open now
LLM Engineering: RAG, Evals & Multimodal Apps
Design retrieval, evaluation, and an evidence-based application.
- Open now
Agentic AI: Tools, Workflows & MCP
Add bounded tools, approval steps, and failure recovery.
WHAT IS MOVING IN AI
The frontier, mapped.
Reasoning models, agents, post-training, evals, multimodal and safety, each explained with what to learn, which course covers it and the papers behind it.
FoundationsHow modern AI works3 courses openBuildLLM engineering and RAG2 courses openBuildAgents, tools and MCP1 course openFrontierReasoning models1 course openFrontierFine-tuning and post-training1 course openMeasureEvaluation and reliability2 courses openFrontierMultimodal AI1 course openResponsibleAI safety and alignment3 courses open
START WITH YOUR GOAL
Where would you like to go?
Choose a topic to explore. Check each course for its level and requirements.
THE HANDS-ON LEARNER
Learn Python & data
Turn numbers and ideas into code. Look for a course that fits your programming experience.
Python · Data · Problem solvingExplore Python coursesTHE ASPIRING BUILDER
Build with language models
Explore prompts, retrieval and the building blocks of useful AI applications.
Prompts · Retrieval · AI workflowsExplore LLM coursesYOUR NEXT CHAPTER
Find a course. Start something.
Explore the available course summaries and see what suits you.
AI Safety & Alignment for Builders
Threat-model LLM applications, defend against direct and indirect prompt injection with controls enforced in code, handle personal data carefully, and build a red-team suite. Offline labs; no paid API.
By Admin
Free courseView course
Post-Training: Fine-Tuning, RLHF & DPO
Learn how supervised fine-tuning, RLHF, DPO, and LoRA turn a pre-trained model into an assistant, audit preference data, and decide when fine-tuning is worth it. Offline labs; no GPU required.
By Admin
Free courseView course
Reasoning Models & Test-Time Compute
Understand chain-of-thought, test-time compute, self-consistency, and verifiers. Measure when a reasoning model is worth its cost and latency, using offline labs that need no paid API.
By Admin
Free courseView course
Agentic AI: Tools, Workflows & MCP
Design bounded tool workflows, approval gates, retry-safe actions, and MCP contracts. Test a research-and-draft assistant without sending real messages.
By Admin
Free courseView course
LLM Engineering: RAG, Evals & Multimodal Apps
Build a transparent retrieval baseline, validate citations and access, design multimodal workflows, and evaluate an evidence-based tutor prototype.
By Admin
Free courseView course
Large Language Models: From Prompts to Practice
Understand tokens and context, build reusable prompts, work with source documents and JSON, and evaluate a prompt portfolio honestly.
By Admin
Free courseView course
BUILT FOR PRACTICE
Learn it by building it.
Python in the browser
Multi-file projects, input and arguments, run in an isolated sandbox. Nothing to install.
Reviewed projects
Courses include a portfolio project, scored against a published rubric by a person, not a bot.
Lesson-grounded help
Where enabled, ask about the lesson you are on. Answers link back to the lesson text and never grade you.
Certificates
Finish a course to get a printable certificate with a shareable verification page.
TERM OF THE DAY
Mixture of experts
An architecture that routes each token to a few specialised sub-networks, so only part of the model runs per token.
Browse the glossaryBEFORE YOU BEGIN
A clear start.
Answers to the practical questions, so you can focus on learning.
Read the full guideHow do I start learning?
Browse the course catalogue, open a course, and review its curriculum, requirements, price and access period. Create an account or sign in to enrol. Your enrolled courses appear in My learning.
Can I write and run code here?
Lessons with reviewed interactive material include a Python code workspace. Open Code workspace, edit a file and select Run Python. Your draft stays in this browser; export your project to keep a separate copy. The workspace does not provide a full system terminal.
Are all courses free?
Free courses are labelled Free. This preview supports enrolment in published free courses with available lessons. Paid purchases are not enabled here yet.
Will every course include a certificate?
Certificate availability depends on the course and its completion requirements. Certificates are not issued by this preview. A completion certificate does not imply accreditation or a professional qualification.
How long can I access a course?
Access depends on the course and your enrolment. Check the access period on the course page and any expiry date in your account. Access is not automatically lifetime for every course.
How do lesson questions and practice work?
Read the lesson, answer any practice question, and use the discussion to ask questions or share a reflection. Practice feedback appears after you submit an answer. Mark each lesson complete when you finish.