A free course that helps you use AI tools to write better code, faster.
Join Slack • #course-ai-dev-tools-zoomcamp Channel • Telegram Announcements • Course Playlist • FAQ
| Resource | Link |
|---|---|
| Course materials | GitHub repository |
| Video lectures | YouTube playlist |
| Documentation | Zoomcamp Logistics · AI Dev Tools Zoomcamp |
| Course platform (deadlines, homework) | courses.datatalks.club |
| Slack channel | #course-ai-dev-tools-zoomcamp |
| Announcements | Telegram |
| FAQ | FAQ document |
The AI Dev Tools Zoomcamp is a free, hands-on course that teaches you how to use AI tools to write better code, faster. You'll work with coding assistants, agents, the Model-Context Protocol, AI for testing and CI/CD, and low-code automation, building real projects along the way.
This course is for anyone who wants to use AI tools to help with coding. You don't need any AI experience to start, just curiosity about using AI tools in your work.
- A basic ability to program (Python, JavaScript, or similar)
- No prior experience with AI tools is required
There are two ways to follow the course: live and self-paced.
| Live Cohort | Self-Paced | |
|---|---|---|
| Start | 2026 (date TBA) | Anytime |
| Lectures | Pre-recorded | Pre-recorded |
| Homework | Graded | Available but not scored |
| Leaderboard | ✅ Yes | ❌ No |
| Peer Review | ✅ Yes | ❌ No |
| Certificate | ✅ Yes | ❌ No |
| Cost | Free | Free |
| Register | Sign up here | Just start learning! |
Important
"Live cohort" does not mean live classes. All lectures are pre-recorded. "Live" means working alongside others with deadlines, scored homework, a leaderboard, peer review, and a certificate at the end.
Self-paced steps:
- Follow the materials on GitHub
- Ask questions and share progress in Slack
- Do the homework (self-checked) and build a project for your portfolio
- AI-assisted development with a Snake game example (React + JS)
- Chat applications: ChatGPT, Claude, DeepSeek, Microsoft Copilot
- Coding assistants / IDEs: Claude Code, GitHub Copilot, Cursor, Pear
- Project bootstrappers: Bolt, Lovable
- Agents: Anthropic Computer Use, PR Agent, and others
- Use a coding assistant for an end-to-end project
- Build Snake in React/TS
- Define the API with OpenAPI
- Generate a FastAPI server from the OpenAPI specs
- Add CI/CD
- Deploy the application
- Enhancing AI assistants with tools
- Core servers: GitHub, Filesystem, DB/SQL, HTTP/API, CI
- Practical workflows: repo triage, PR summarization, scripted edits, data queries
- Local vs. remote servers
- Security and permissions
- Build your own coding agent that can scaffold and extend projects
- Use a Django template as the base project
- Learn how agents act as project bootstrappers
- Explore multiple agent orchestration frameworks
- Outcome: a Django app created and modified by your AI agent
- AI-assisted PR reviews/summaries and change-risk hints
- Automated test generation, coverage gates, and LLM evals in CI
- Release notes, changelog drafting, and deployment runbooks
- Incident postmortems and on-call copilots
- Install n8n
- Create posts for LinkedIn
- Tailor your CV for a specific position
The final project applies everything from the course in an end-to-end build of your own, followed by peer review.
Certificates are awarded to learners who complete the final project and the required peer reviews during a live cohort. See Certification for how certification works and how to get your certificate.
This course fundamentally changed how I approach AI development. I moved from "building models" to designing AI-assisted systems that are faster to ship and easier to iterate on.
During the course, I built:
- A portfolio optimization tool powered by AI-assisted development
- A full-stack application using ChatGPT, Lovable, and Antigravity
- A structured GitHub project with clean documentation and reproducible workflow
What changed for me: I now think in terms of system design rather than isolated scripts. I learned how to structure AI tool usage, validate outputs, and integrate generated code into disciplined engineering workflows. The biggest shift was moving from experimentation to controlled, production-oriented iteration. I can now prototype and deploy AI-enabled tools significantly faster without sacrificing rigor.
— Yann Pham-Van, Freelance Data Scientist
The course taught me how to use coding agents effectively, debug issues, and gave me exposure to MCPs, tools, and prompts. It helped me conceptualize any idea into a working prototype. And finally, it helped me land a job after a long career break!
— Revathy Ramalingam, Senior Software Engineer at Yalabs Solutions
During the course I built a Finnish learning website which helps English users learn and practice reading, writing, listening and speaking skills for the Finnish language. I used the Antigravity IDE with Gemini 3 Pro and Claude Opus, a Context7 documentation MCP server, TypeScript and Python, Next.js and FastAPI, SQLite, and CI/CD with GitHub Actions.
What changed for me: learning a systematic way to think about requirements and design an application before building and testing components iteratively, packaging frontend and backend into a single container for easier deployment, and getting comfortable debugging frontend and backend tests during integration and deployment.
— Kaiquan Mah, Data Scientist at Total eBiz Solutions
Join the #course-ai-dev-tools-zoomcamp channel on DataTalks.Club Slack for discussions, troubleshooting, and networking.
To keep discussions organized:
- Follow our guidelines when posting questions.
- Review the community guidelines.
Share your progress as you go, using the hashtag #aidevtools and tagging Alexey Grigorev or DataTalksClub. It helps you learn better, builds your network, and earns you bonus points. See the learning in public guide.
Interested in supporting our community? Reach out to alexey@datatalks.club.
A few common questions. For everything else, see the full AI Dev Tools Zoomcamp FAQ.
Q: Is this course really free?
A: Yes. All videos, materials, and homework are free and open-source.
Q: Do I need prior experience?
A: No AI experience is needed. A basic ability to program in Python, JavaScript, or a similar language is enough.
Q: What does "live cohort" mean? Are there live classes?
A: No mandatory live classes. All lectures are pre-recorded. "Live" means deadlines, scored homework, a leaderboard, peer review, and certificate eligibility.
Q: Can I take it self-paced, and will I get a certificate?
A: Yes, you can start anytime. Certificates require completing the final project and 3 peer reviews during a live cohort.
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