Choose Your Track
Six quick questions to find where you should start, then a checklist for every track that fills in as you read.
A retired teacher in Ipoh, a finance analyst in Kuala Lumpur and a final-year computing student in Johor Bahru all want to “learn AI” — and they need completely different first pages. Starting too low is boring; starting too high is discouraging, and both make people quit. This page helps you pick an honest starting point, then keeps a checklist of your progress so you always know what to read next.
How the site is organised
AI Journey has five tracks that build on one another, plus a history section anyone can read at any time:
- Track 1 · Novice — what artificial intelligence is, how it learns and how to talk to it. No code, no maths.
- Track 2 · Practitioner — using AI well for writing, research, data and automation, still without code.
- Track 3 · Builder — Python, data, classic machine learning and applications built on language-model APIs.
- Track 4 · Advanced — the maths and internals: neural networks, Transformers, training and evaluation.
- Track 5 · Expert — research at the frontier: reasoning, interpretability, alignment and open problems.
- The Story of AI — from ancient automata to ChatGPT, in seventeen chapters.
The Builder, Advanced and Expert tracks are still being written. Their checklists below show the pages that are already live and will grow as new pages are published.
Find your starting point
Answer every question honestly — there are no wrong answers, and you can change your mind. Your leanings appear when all six are answered.
1. How much have you used AI tools so far?
2. What do you most want out of this?
3. How comfortable are you with writing code?
4. And with mathematics?
5. How much time can you give each week?
6. Which of these sounds most fun?
Start with Track 1: Novice
Begin at the beginning: what AI is, how it learns, how to talk to it and where it goes wrong. No code, no maths — about ten minutes a page.
Start: Your AI Journey Starts Here → · Track overview · Your checklist
Start with Track 2: Practitioner
You know the basics; now make AI a dependable tool for writing, research, data and automation — still without code. Skim the Novice checkpoint first if you are unsure.
Start: The Practitioner’s Road → · Track overview · Your checklist
Start with Track 3: Builder
Time to make things: Python, data, classic machine learning, then apps on language-model APIs. The first pages assume no prior coding.
Start: The Builder’s Road → · Track overview · Your checklist
Start with Track 4: Advanced
Open the box: the maths, neural networks from scratch, Transformers, training and evaluation. Comfortable Python is assumed.
Start: The Advanced Road → · Track overview · Your checklist
Start with Track 5: Expert
Read, reproduce and question current research: reasoning, interpretability, alignment and the open problems.
Start: The Expert’s Road → · Track overview · Your checklist
Start with The Story of AI
Begin with the people and ideas: eight decades of summers, winters and breakthroughs. It is the gentlest way in and gives every later page context.
Start: AI History at a Glance → · Section overview · Your checklist
A tie means two starting points suit you equally — pick the one that sounds more fun. You can always move up or down a track.
Making a path stick
Consensus Learning research consistently finds that short sessions spread over days, and testing yourself rather than rereading, beat cramming. So read one or two pages at a sitting, use each page’s quiz and reflection, and come back the next day. Mark a page as read at its end and the checklists below fill in automatically. Your progress is stored only in this browser; My Journey gathers it in one place.
Track 1 · Novice checklist
Track 2 · Practitioner checklist
Track 3 · Builder checklist
Track 4 · Advanced checklist
Track 5 · Expert checklist
The Story of AI checklist
Themed paths
Not everyone needs a whole track. These shorter routes cut across the site for particular readers.
AI for managers
For team leaders deciding how their people should use AI: what it can and cannot do, how to choose tools, how to check work, and how to set sensible rules.
AI for students and teachers
For learners and educators: how language models work, how to use them to study rather than to skip studying, and how to spot when they are wrong.
A weekend crash course
Ten pages, about two hours in total — five on Saturday, five on Sunday — that take you from zero to a confident, careful user.
// Pause and reflect
- What do you want to be able to do with AI in three months’ time that you cannot do today?
- When in your week will you realistically read — and how many pages fit in that slot?
- Who could you share a path with, so you can talk about what you read?
Your notes are saved privately in this browser.
Key terms on this page
Sources & further reading
- John Dunlosky, Katherine Rawson, Elizabeth Marsh, Mitchell Nathan & Daniel Willingham, “Improving Students’ Learning With Effective Learning Techniques”, Psychological Science in the Public Interest 14(1) (2013).
- Henry Roediger & Jeffrey Karpicke, “Test-Enhanced Learning: Taking Memory Tests Improves Long-Term Retention”, Psychological Science 17(3) (2006).
- Nicholas Cepeda, Harold Pashler, Edward Vul, John Wixted & Doug Rohrer, “Distributed practice in verbal recall tasks: A review and quantitative synthesis”, Psychological Bulletin 132(3) (2006).
- For courses and books beyond this site, see Courses, Books and Resources.