Domain expert → AI practitioner

Use your expertise to apply AI to the real problems of your field.

This is you

You know a field deeply: health, finance, law, logistics, education or another one. You see where AI could help, and where it could do harm. You are not a programmer, or you write only a little code.

Where you are going

An AI practitioner in your field

Find the problems in your field where AI helps, and the ones where it does not
Prepare the data of your field, and build a first model with it
Make an AI assistant answer from the documents of your field
Test AI answers with your expert judgement, and show the evidence

3 stages
13 courses, 13 available now
About 168–224 hours of study
11 certificates of completion + 1 concepts certificate

How much code you need

No code
Read code
Some code
Code is central

Your knowledge of the field is the hard part, and you already have it. You will write some Python, but you learn early on this path how to let an AI assistant write it with you. You do not need to become a software engineer.

Your route, stage by stage

Each stop is a course. Its shape tells you how much of it to take.

Full course. The whole course, with its practice. It leads to a certificate of completion.
Concepts. Only the concept modules, read without running code. They lead to a concepts certificate.
Optional. Useful for some people. You do not need it, and it is not counted in the time.

You, today
Stage 1 Find your footingabout 37–50 h
1. AI Career Foundations: Roles, Skills and Roadmaps
  Full course4–6 h
  See the roles that domain experts lead, and choose yours.
  Focus: The lesson “Domain knowledge in practice” is written for you.
2. Python for Practical AI Engineering
  Full course18–24 h
  Enough Python to read, run and change small programs.
  Focus: Take your time with the practice. Every lesson shows the full code.
3. Git and Team Development Essentials
  Full course5–7 h
  Save your work, and share it with engineers.
4. AI-Assisted Software Development
  Full course10–13 h
  Let an AI assistant write code with you, and check what it writes.
Stage 2 Work with data and modelsabout 50–64 h
1. Essential Mathematics and Statistics for AI
  Full course12–16 h
  No code in this course: it teaches you to read and judge numbers.
2. SQL and Data Preparation for AI
  Full course12–16 h
  The data of your field, clean and ready.
3. Machine Learning: From Problem to Reliable Model
  Full course26–32 h
  Your expertise matters most here: you frame the problem and judge the model.
  Focus: Modules 1 to 3 and 7 use your field knowledge the most.
Stage 3 Bring AI to your fieldabout 81–110 h
1. LLMs: How They Work and How to Choose One
  Full course8–12 h
  Know what LLMs can and cannot do for your field.
2. Building Reliable Applications with LLM APIs
  Full course22–30 h
  Build small tools on LLMs, with an assistant’s help.
3. RAG: Building AI That Uses Your Data
  Full course24–32 h
  Make AI answer from the documents of your field.
4. Evaluating and Testing AI Systems
  Full course20–26 h
  Your strongest skill: you know what a correct answer is.
5. AI Security, Privacy and Responsible Design
  Concepts7–10 h
  Privacy and risk matter most in regulated fields.
  Focus: Modules 1, 2, 4 and 5. Read the code and the notebooks; you do not need to run them.
6. Document and Multimodal AI
  Optional16–22 h
  For fields with many forms, scans and PDFs.
You arriveAn AI practitioner in your field

Earn certificates as you go

You do not need to finish the whole path to show what you learned. Each full course gives a certificate of completion, and each concepts course gives a concepts certificate. Share each one when you get it.

When you are ready, prepare for the interview: Interview preparation: Data Scientist

Make it my path All learning paths

Choose this goal in your profile. My learning then shows your progress on this route, and the weeks it takes at your pace.