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AIE4 months · 16 weeks · Enrolling

Stop using AI. Start building it.

Four months from Python basics to AI products that work: LLM apps, RAG, agents and automations, deployed and doing real jobs.

Apply now See the syllabus

Free to apply. No experience needed. Fees and payment plans at registration.

16weeks
4levels
4projects you ship
0experience needed
The journey

Four months. Four levels.

You leave able to build, evaluate and deploy AI applications, with a portfolio of working AI products to prove it. Each month takes you up one level, and each level ends with a project you ship.

1
Month 1 · Beginner

Python for builders

The language behind modern AI, learnt by building small tools that are useful from day one.

You ship: Data Assistant CLI
2
Month 2 · Builder

LLMs in practice

How large language models work, and how to make them reliable inside your own apps.

You ship: SME AI Assistant
3
Month 3 · Professional

RAG and agents

Give AI your own knowledge, let it use tools, and prove it is right before anyone relies on it.

You ship: Knowledge Assistant
4
Month 4 · Job-ready

Automate and deploy

Turn AI into automations and services a business can depend on.

You ship: Capstone: your pod's AI product
Anatomy of an AI product

Every layer. Built by you.

A real AI product is far more than a prompt. It has an interface, an orchestrator, tools, your own data, the models, and checks that prove it works. You build each layer, in the order that makes sense.

Interface

Chat, voice and image interfaces people actually use: Streamlit, Gradio, WhatsApp, an API.

Months 2 and 4
Orchestration

Agents and workflows that plan, call tools and hand off to a human: LangGraph, n8n.

Months 3 and 4
Tools and data

Your own knowledge, searchable: embeddings, vector databases and RAG with citations, plus APIs and MCP tools.

Month 3
Models

GPT, Claude, Gemini and open models: how they work, what they cost, and how to pick one.

Month 2
Evaluation and safety

Proof it works: test sets, tracing, guardrails, prompt-injection defence and data privacy.

Month 3
Foundation

Python, APIs, data and Git: the base every AI engineer stands on.

Month 1
The syllabus

Week by week, beginner to job-ready.

Every week: what you learn, and one thing you build with it. Tools that companies hire for today, taught in the order that makes sense.

The language behind modern AI, learnt by building small tools that are useful from day one.

node_01Beginner

Python from zero

  • Setting up Python, VS Code and uv
  • Variables, types and control flow
  • Notebooks with Jupyter
You buildYour first useful script
node_02Beginner

Functions, data and files

  • Functions, lists and dictionaries
  • Reading and writing files, JSON and CSV
  • Packages and virtual environments
You buildA contact-list cleaner
node_03Beginner

APIs and data

  • Calling APIs with httpx
  • Keeping secrets safe in .env files
  • pandas for everyday data work
You buildA live exchange-rate report
node_04Beginner

Python for real apps

  • Type hints and Pydantic models
  • async Python
  • Git and GitHub
You buildYour Data Assistant, on GitHub
M1
Month 1 project: Data Assistant CLI

A Python tool that pulls live data from an API, cleans it with pandas and writes a clear report.

How large language models work, and how to make them reliable inside your own apps.

node_05Builder

How LLMs really work

  • Tokens, context windows and temperature
  • Why models hallucinate
  • GPT, Claude, Gemini and open models: choosing well
  • Cost and speed
You buildA notebook comparing three models
node_06Builder

Prompting and structured output

  • System prompts and few-shot examples
  • Getting clean JSON out of a model
  • Function and tool calling
You buildAn invoice and receipt extractor
node_07Builder

Building chat apps

  • The OpenAI, Anthropic and Gemini SDKs
  • Streaming replies and conversation memory
  • Quick interfaces with Streamlit and Gradio
You buildA customer-support chatbot
node_08Builder

Multimodal AI

  • Images in, answers out
  • Speech to text and text to speech
  • Generating images
You buildA voice-and-image assistant
M2
Month 2 project: SME AI Assistant

A chat assistant for a Nigerian small business that answers customers, reads images and replies in a consistent voice.

Give AI your own knowledge, let it use tools, and prove it is right before anyone relies on it.

node_09Professional

Embeddings and vector databases

  • What embeddings are
  • Chunking documents well
  • pgvector and Chroma
You buildSemantic search over real documents
node_10Professional

Retrieval-augmented generation (RAG)

  • Retrieval pipelines and re-ranking
  • Answers with citations
  • LlamaIndex and LangChain
You buildChat with your documents
node_11Professional

AI agents and tool use

  • How agent loops work
  • Tools and the Model Context Protocol (MCP)
  • LangGraph
  • Guardrails and keeping a human in the loop
You buildA research agent
node_12Professional

Evaluation, safety and cost

  • Test sets and LLM-as-judge
  • Tracing with Langfuse
  • Prompt injection and data privacy (NDPA)
  • Keeping cost and latency down
You buildAn evaluation report for your assistant
M3
Month 3 project: Knowledge Assistant

A "chat with your documents" app that answers with citations, measured against a test set you build.

Turn AI into automations and services a business can depend on.

node_13Job-ready

Workflow automation

  • n8n, Make and Zapier
  • Webhooks and triggers
  • AI inside workflows: email triage, lead handling, WhatsApp
You buildAn automated lead handler
node_14Job-ready

Shipping AI services

  • APIs with FastAPI
  • Docker and deployment on Railway or Render
  • Background jobs and rate limits
You buildYour assistant, live as an API
node_15Job-ready

Open-source and local models

  • Hugging Face and Ollama
  • Small and local models
  • When fine-tuning helps (and when it does not)
You buildA private, local AI tool
node_16Job-ready

Capstone and career

  • Launch week with your pod
  • Demo day
  • Portfolio and AI engineering interviews
You buildYour live AI product and portfolio
M4
Month 4 project: Capstone: your pod's AI product

Your pod builds and launches an AI product that solves a real problem, then demos it to an audience.

Proof of work

What you ship.

Employers hire what they can see. You finish with three projects and a capstone built with your pod, all in your portfolio.

01

Data Assistant CLI

A Python tool that pulls live data from an API, cleans it with pandas and writes a clear report.

Month 1
02

SME AI Assistant

A chat assistant for a Nigerian small business that answers customers, reads images and replies in a consistent voice.

Month 2
03

Knowledge Assistant

A "chat with your documents" app that answers with citations, measured against a test set you build.

Month 3
04

Capstone: your pod's AI product

Your pod builds and launches an AI product that solves a real problem, then demos it to an audience.

Capstone · Month 4
The stack

Tools teams use today.

PythonLanguage
uv and JupyterWorkflow
pandasData
OpenAI APILLM
Claude APILLM
Gemini APILLM
LangChain and LlamaIndexRAG
LangGraphAgents
MCPTools
pgvector and ChromaVector DB
LangfuseEvaluation
n8nAutomation
FastAPIServices
Hugging Face and OllamaOpen models
How you learn

Never alone.

The same Virtual Uni every Webbo3 student learns in.

Live virtual classes

Real lecturers, live, Monday to Saturday. Ask questions as you go.

Kemi, your AI lecturer

Lessons and help any time of day, at your pace, in plain words.

A pod of ten

Your team for the whole programme. You build, review and ship together.

PO

The Pod Office

Your virtual office, with the tools for the job built in, so you practise the way you will work.

SP

Skill Points and a certificate

Every class and project earns SP. Finish strong and your certificate unlocks.

By the end

You will be able to.

  1. Write solid, modern Python
  2. Explain how LLMs work and pick the right model for a job
  3. Build chat, document and multimodal AI apps
  4. Build RAG systems that answer with sources
  5. Build agents that use tools safely
  6. Automate business workflows and deploy AI services

Roles this prepares you for

  • AI Engineer (Junior)
  • AI Automation Specialist
  • LLM Application Developer
  • AI Solutions Consultant
  • Python Developer
  • AI Product Builder

Plus a portfolio of real projects, a CV and interview preparation in the final month.

Questions

Asked and answered.

Do I need to be good at maths or coding?

No. You start with Python from zero. We explain how AI works in plain words and focus on building.

Is this just about using ChatGPT?

No. You build your own AI applications on top of models through their APIs, the way AI engineers work.

Do I have to pay for AI APIs?

We start on free tiers and open models. When a paid API helps, the cost for a project is small and we show you how to keep it that way.

How much time does it take?

Plan for 10 to 12 hours a week: live classes, practice with Kemi and your pod project.

How are classes run?

Live virtual classes with your lecturers, lessons with Kemi (our AI lecturer) any time, and weekly projects with your pod of ten classmates in the Pod Office.

Do I get a certificate?

Yes. Finish the classes, earn your Skill Points and submit your projects, and your Webbo3 certificate unlocks, with your projects as proof of work.

How much does it cost?

Fees and payment plans are shown when you register, after your application. Apply first: it is free and takes a few minutes.

build the AI, don't just use it

Apply in a few minutes. It is free to apply, and fees and payment plans are shown at registration.

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