The beginner pilot

Our first courses are written and reviewed. Now we need learners to take them, and to tell us every place where a beginner gets stuck.

The pilot courses

AI Career Foundations: Roles, Skills and Roadmaps · 18 lessons · 4–6 hr
Python for Practical AI Engineering · 18 lessons · 18–24 hr
Essential Mathematics and Statistics for AI · 18 lessons · 12–16 hr
SQL and Data Preparation for AI · 18 lessons · 12–16 hr
Machine Learning: From Problem to Reliable Model · 27 lessons · 26–32 hr
Deep Learning and Transformers Explained · 23 lessons · 28–36 hr
LLMs: How They Work and How to Choose One · 16 lessons · 8–12 hr
Building Reliable Applications with LLM APIs · 19 lessons · 22–30 hr
RAG: Building AI That Uses Your Data · 18 lessons · 24–32 hr
AI Agents and Workflow Orchestration · 18 lessons · 22–30 hr
Evaluating and Testing AI Systems · 19 lessons · 20–26 hr
Fine-Tuning and Adapting AI Models · 12 lessons · 10–14 hr
Document and Multimodal AI · 19 lessons · 16–22 hr
Git and Team Development Essentials · 15 lessons · 5–7 hr
AI-Assisted Software Development · 15 lessons · 10–13 hr
Build and Deploy Your First API · 21 lessons · 11–15 hr
Deploying AI Models for Real Users · 15 lessons · 8–12 hr
Docker and Cloud Essentials for AI Developers · 15 lessons · 7–10 hr
CI/CD, Testing and Safe Releases · 15 lessons · 8–12 hr
Databases and Storage for AI Applications · 16 lessons · 16–22 hr
Scaling APIs and AI Workloads · 19 lessons · 17–23 hr
Frontend Architecture and Performance for AI Apps · 19 lessons · 20–26 hr
Authentication, Permissions and Multi-Tenant Applications · 19 lessons · 16–22 hr
AI Security, Privacy and Responsible Design · 18 lessons · 18–24 hr
MLOps and LLMOps: Operating AI in Production · 19 lessons · 16–22 hr
AI System Design and Cost Engineering · 19 lessons · 16–22 hr
Portfolio Capstone: Build, Evaluate and Ship an AI Product · 22 lessons · 30–45 hr
MCP: Connect AI Applications to Tools and Data · 22 lessons · 16–21 hr
Leading AI Products and Teams · 22 lessons · 12–16 hr

Every course is free. You can take one course, or all of them. Python for Practical AI Engineering is the best start if you are new to programming.

How to take part

Sign in with Google, GitHub, LinkedIn or your email address. The pilot needs an account, so that your reports belong to you and you can delete them.
Enroll in a pilot course, and take the lessons in order. Do the practice on your own computer, as the lesson says.
Report as you go. At the end of each lesson, open the form Something unclear or not working? Tell us every word that the lesson did not explain, every step that was missing or did not work, and every setup problem. Copy the error message if there is one. Small things count: they stop other beginners too.
Tell us the time that each lesson took you, in the same form. It helps us to plan the courses honestly.
Mark each lesson complete when you finish it, so that we can see where learners stop.

What a good report looks like

"In step 4, the lesson says activate the environment, but it does not say what to type on Windows. I typed source .venv/bin/activate and got The term 'source' is not recognized."

One problem per report is best. You can send as many reports as you want.

What happens to your feedback

The course team reads every report, and fixes the lesson. You may see the change in the lesson a few days later.
Your reports are saved with your account and the lesson, in the same database as your progress. They are not public.
You can download them with the rest of your data, or delete them with your account, on your profile page.
Do not put passwords, API keys or other personal data in a report. The privacy policy explains how we handle your data.

Start with AI Career Foundations: Roles, Skills and Roadmaps All courses