How long is Multimodal AI?

7.5 hours of recorded sessions across 5 modules, taken at your own pace. 4 of the modules end in work Nova grades.

Is Multimodal AI live or self-paced?

Self-paced. The sessions are recorded by an instructor who does this work, and Nova, Square 1's AI tutor, grades every checkpoint and project against a rubric you can read.

How much does Multimodal AI cost?

A$45 for the founding intake, paid once; the standard price is A$90. It is the same price in every country. Founding students keep their founding price on any second programme.

What will I build in Multimodal AI?

3 deployed projects: A document understanding feature, An audio pipeline and A multimodal product. Each is graded against a published rubric and kept on a record an employer can open at /verify.

What skills does Multimodal AI teach?

vision models, speech, image generation and safety. It prepares you for roles such as AI engineer and Product engineer (AI).

Does Multimodal AI guarantee a job?

No. No placement rate is published because there are no graduates to count yet. What you leave with is graded, deployed work on one record an employer can open and run, and a hiring plan you keep with the career agent.

On demandRecorded by an instructor, graded by Nova

Multimodal AI

Name: Multimodal AI
Price: 45.00 AUD
Availability: PreOrder

Vision, audio and image generation in one product.

Multimodal AI is a self-paced online course: 7.5 hours of recorded sessions across 5 modules, with 4 graded by Nova, Square 1's AI tutor. The founding price is A$45, down from A$90. Start any time and keep your own pace.

Taught with

PythonClaudeGeminiChatGPT

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Module by module

Module 1Multimodal models today60 min
Module 2Vision input: documents and images90 min · graded
Module 3Audio: transcription and speech90 min · graded
Module 4Image generation and editing90 min · graded
Module 5Project: a multimodal product120 min · graded

recorded hours: 7.5
modules, in order: 5
deployed projects: 3
graded checkpoints: 4

Who it is for, and what you receive.

Developers in Python who have made one LLM API call and want a product that sees and hears.

Before you start

Python; one LLM API call before.

Skills

vision modelsspeechimage generationsafety

1
The recorded sessions
Taught by an instructor, taken in order at your own pace, yours for twelve months.
2
Graded work after every module
Exercises and projects marked by Nova against a rubric you can read, with what you did well and what to fix.
3
The projects
Deployed and graded, each one on your record with its repository.
4
Your hiring plan
The same six-part plan the bootcamps use, run with the career agent.
5
The record and the certificate
Every grade at square1ai.com/u/{handle}; a credential ID that resolves at square1ai.com/verify.
6
Nova as your tutor
Help that is about your actual work, because it has read all of it.

The content plan, module by module.

7.5 recorded hours from an instructor, taken in order at your own pace. Nova grades the work at the end of each module.

Vision, audio, generation
Trade-offs
Costs

The projects.

Each is deployed and graded by Nova against a rubric you can read before you start.

Project 1
A document understanding feature
A fictional property-management office receives scanned lease applications as photos and PDFs. Build a feature that reads each one with a vision model and returns structured fields, then measure accuracy against a hand-labelled set that includes skewed and handwritten pages.
You hand in
- Extraction feature in a repository
- Hand-labelled test set
- Field-level accuracy report
The rubric requires
Extraction accuracy is measured per field on the labelled set and reported.
Project 2
An audio pipeline
Using a public set of recorded meetings or podcasts with reference transcripts, build a pipeline that transcribes each recording and writes a short summary. Report word error rate against the references, and check summaries for claims the audio never made.
You hand in
- Pipeline repository
- Word error rate report
- Summary quality check
The rubric requires
Word error rate is reported against the reference transcripts.
Project 3
A multimodal product
Combine what you built into one deployed product, for example a tool for the property office that reads a photographed document, answers spoken questions about it and generates an illustrative image. Generated images must pass a safety check before anyone sees them.
You hand in
- Deployed product URL
- Source code repository
- Evaluation results per modality
- Safety check on generation
- Recorded demo
The rubric requires
The product is deployed and every generated image passes through a safety check.

What you can do at the end.

A product that sees and hears, and three graded projects on your record.

1
Choose among multimodal models for a task.
2
Build document and image understanding features.
3
Build audio transcription and speech pipelines.
4
Generate and edit images with a safety check.
5
Ship a multimodal product.

Roles this prepares you for

AI engineerProduct engineer (AI)

No placement rate is shown, because there are no graduates to count yet. The roles above are what the projects are built for.

Your record at the end.

Every exercise and project is graded by Nova against a rubric you can read, and every grade is kept on one page an employer can open and run. This is what the programme writes to it.

Graded, line by line: Nova reads every submission against the brief and the rubric and returns a score, what you did well and what to fix.
Module by module: Each module ends in graded work; the next opens when you are ready, on your own schedule.
Nova remembers: Help is about your actual work, because the tutor has every submission and every failed exercise of yours.
One page an employer can run: Every grade and project at /verify. An employer opens it and runs the code.

Record, Multimodal AIExample

Project
A document understanding feature
Extraction accuracy is measured per field on the labelled set and reported.
Graded
Project
An audio pipeline
Word error rate is reported against the reference transcripts.
Graded
Project
A multimodal product
The product is deployed and every generated image passes through a safety check.
Graded
Every module
4 graded checkpoints
Each module ends in work Nova grades line by line against a rubric you can read.
Graded

The entries, not the grades: those are yours to earn. The page lives at /verify and an employer needs no account to open it.

5 entries, at your own pace. Reserving a spot holds the founding price.

Pre-order

How we help you find a job.

Proof, not a certificate: the projects you deployed are the thing you show, and the tools below are yours to use.

The course booklet

The whole plan on a few pages: the parts, the projects, the gates and the price. We email it to you.

Get the booklet

1
Your hiring plan
The same six-part plan the bootcamps use: target roles, the gap map, the proof to send, weekly actions, an interview log, the outcome. You run it with the career agent.
2
A record an employer can run
Your graded projects on /verify. An employer opens it and runs the code.
3
The career agent
Paste a real job posting at /career and it maps the role to your graded work and what to do next.
4
The roles directory
Every role we prepare people for, what it pays and what it asks, at /roles.
5
A path to the live cohort
If you want the instructor, the gates and the hiring sprint, the bootcamp on the same subject is one waitlist away.

Questions people ask.

About Multimodal AI, answered from the plan on this page.

Founding spots are open. Hold yours.

Recorded by an instructor who does this work, graded by Nova. Spot holders hear the opening date first, and nothing is charged before you confirm.

A product that sees and hears
Three graded projects on your record

A$45~~A$90~~