An 8-week live beginner cohort — Python, working with data, and the foundations every AI role assumes. The most common place to start, taught from scratch.
Everyone starts somewhere. This is a real beginning, not a tour.
Beginner AI Foundations is eight live weeks building the genuine fundamentals every later course assumes: Python, working with data, version control, and how machine learning actually works.
No prior coding required. A small cohort taught live by a senior practitioner, with the same production-first mindset that runs through everything we do.
You finish ready for an intermediate cohort, not just aware that one exists.
Not here. We start at the environment and the command line, and nobody is expected to arrive fluent.
Loading, cleaning and interrogating real data is eighty per cent of every AI role. We spend real time on it rather than skipping to the model.
You leave with a project in version control that you built and can defend — which is what an intermediate cohort actually assumes.
Movement I · Weeks 01–04Get fluentEnvironment, Python, real data and version control — the ground everything else stands on.
Movement II · Weeks 05–07Get modellingHow a model is trained and judged, and how modern AI fits alongside it.
Movement III · Week 08Prove itA small end-to-end project of your own, shipped and defended.
Getting set up and thinking in codeYour environment, the command line, and the mental model of programming. We start from zero and remove the intimidation before we touch anything hard.
A working environment and the first program you wrote yourself.
Python fundamentalsVariables, data types, control flow and functions — the building blocks, taught through small real problems rather than toy puzzles.
Python you can read and debug without copying from a tutorial.
Working with dataLoading, cleaning and exploring real datasets with pandas. The unglamorous eighty per cent of every data and AI job, done properly.
A messy real dataset cleaned by you, and questions you can now answer with it.
Version control and collaborationGit and GitHub the way teams actually use them, so your work is safe, shareable and professional from the first week.
A repository someone else could clone, read and run.
How machine learning actually worksThe intuition behind models — training, evaluation, over-fitting — without the heavy mathematics. You build and assess your first real models.
Your first trained model, and the judgement to say whether it is any good.
Talking to modern AIHow large language models work at a useful level, and how to use them well as a capable beginner: prompting, limits, and where they genuinely fit.
A working sense of what these models are for — and what they quietly get wrong.
Your capstoneYou ship a small end-to-end project of your own and defend it in front of the cohort. Proof you can start, not just that you attended.
A finished project in version control that you can show someone.
In the final two weeks you take a dataset of your choosing — your own, or one we suggest — and carry it end to end: a question worth asking, the cleaning, the analysis, a simple model where it earns its place, and an honest write-up of what you found and what you could not conclude.
You present it to the cohort and your instructor. The bar is not sophistication; it is that the work is yours, it runs, and you can defend every choice in it.
No prior coding is required. If you can use a spreadsheet confidently, you can start here.
You need a laptop you can install software on and roughly six to eight hours a week outside the live sessions. We send a short setup guide before week one and run an optional drop-in to get everyone's environment working before the first class.
Frameworks are great. But understanding what they abstract — the loop, the parsing, the failure modes — is what lets you actually debug and design them under pressure.
Amir founded, funds and directs AI Tech Institute, and still teaches. He holds a PhD and has spent more than twenty years building and leading AI, machine learning and data teams in Australian industry — the kind of work the programs here are drawn from rather than adapted to.
He currently leads asset performance and analytics at Synergy, Western Australia's state-owned electricity generator and retailer. Before that he led GenAI and MLOps program delivery at EY, working on platform modernisation for tier-one Australian banks; served as Chief AI Engineer at Hancock Prospecting, where he built enterprise GenAI platforms for executive decision support, led a Snowflake migration, stood up on-premises NVIDIA H100 infrastructure and Kubernetes clusters for production AI, and built a team of twelve engineers; and spent nearly five years as Tech Lead for ML and Data Science at Woodside Energy.
He has been teaching for twenty-one years. He is an Adjunct Associate Professor at the University of Western Australia, teaching quantitative analysis and decision-making at master's level, and holds Databricks University Alliance Faculty status and the AWS Certified AI Practitioner certification. He has published peer-reviewed research in Exploration Geophysics on machine learning for reservoir characterisation, and writes and records openly about the parts of AI engineering most courses skip — agent loops, termination logic and failure modes.
Cohorts are deliberately small so every piece of work gets reviewed properly and every presentation gets heard in the room. If an intake is full we’ll say so and hold your place for the next one — we won’t quietly oversell a cohort.
See all upcoming dates →The prerequisites for this program are in “Who it’s for” above. Our foundation-level programs and workshops assume none. If you’re unsure, ask on a qualification call — we would rather redirect you than take your money for the wrong cohort.
The commitment for this program is shown in the hero and in “Who it’s for”. Cohort programs run live sessions plus practice between them; workshops are a single block with no homework.
The work you shipped — defended in front of the cohort — and an honest recommendation on your next step. Sometimes that is another cohort with us, sometimes it isn’t; we will tell you which.
No. AI Tech Institute is not a Registered Training Organisation and this is not a nationally accredited qualification. It is professional education, assessed on the work you ship, with a certificate of completion from us. We won’t claim accreditation we don’t have.
Yes — we invoice organisations directly and can supply a scope and outcomes summary for your L&D or capability budget. If three or more people from one team want in, talk to us about a private cohort instead.
Full refund any time before the program starts. Once it has started we can’t refund the place, but we will carry you into a later intake where the circumstances warrant it. Ask us — we’re reasonable.
Thirty minutes with us and you’ll know whether this is your right next step — or which of our programs is. No pitch, no payment, and we will say so if the answer is “not this one”.
