Understand and model AI — the “why it works” course. Twelve weeks from data and statistics to machine learning and LLMs, concept before code, for analysts and the technically curious.
Most courses teach you to copy a notebook. This one teaches you why it works.
Understanding why a method works is what lets you adapt when the tools change next month, which they will. Twelve live weeks from data foundations and statistics, through machine learning, to how modern LLMs and retrieval actually function.
It’s the “understand and model” half of our technical spine and the natural foundation before AI Engineering & Production.
Concept first, code second, taught by senior practitioners in a small cohort.
Every framework you learn this year is replaceable. Knowing what it abstracts is what survives the next release.
The hard part is deciding what is worth modelling at all, and being honest about what the data cannot tell you.
You will be taught to state what you do not know before you state what you found. It is the habit that separates practitioners from dashboards.
Movement I · Weeks 01–04GroundReal, messy data and the statistics you will actually reach for.
Movement II · Weeks 05–08ModelSupervised and unsupervised learning from the inside, evaluated honestly.
Movement III · Weeks 09–12Modern AIHow language models and retrieval work, and your own capstone on top of it.
Data foundationsGetting fluent with real, messy data — acquisition, cleaning, joining and the exploratory work that decides what is worth modelling at all.
A cleaned, joined dataset and a written account of what it can and cannot answer.
Statistics you will actually useDistributions, uncertainty, sampling and significance, taught around the mistakes practitioners keep making rather than around exam questions.
The habit of stating uncertainty before stating a result.
Supervised learningRegression and classification from the inside: what the model is optimising, why it fits, and what over-fitting looks like on your own data.
A trained model you can explain line by line, not one you copied.
Evaluation, honestlyChoosing a metric that reflects the decision being made, validation that does not leak, and reporting a result you can defend.
A validation setup that would survive a reviewer looking for leakage.
Unsupervised methods and representationClustering, dimensionality reduction and embeddings — the bridge from classical machine learning to modern language models.
An embedding space you built, inspected, and can reason about.
How large language models workAttention, pre-training and fine-tuning at a level that changes your decisions. Why these models are good at what they are good at.
A mental model accurate enough to predict where a model will fail.
Retrieval and groundingVector search, retrieval-augmented generation, and where grounding genuinely improves an answer rather than hiding the problem.
A retrieval setup measured separately from the generation on top of it.
Your capstoneAn end-to-end analysis or model on data you care about, with the reasoning made explicit and the limits stated.
An argument you can defend — including a clear statement of what it does not show.
You choose a question that matters to you and take it the whole way: sourcing and cleaning the data, the exploratory work, a model where a model earns its place, an evaluation that does not flatter, and a written argument for what you found.
The deliverable is the argument, not the accuracy. You present to the cohort and your instructor, and you are expected to state clearly what your work does not show.
Intermediate. You should be able to write basic Python and read a spreadsheet analytically. Beginner AI Foundations is the usual route in; if you are self-taught, we will talk it through on a qualification call rather than guess.
Expect eight to ten hours a week outside the live sessions. A laptop you can install software on is required; everything else is browser-based or free-tier.
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”.
