If you want to get noticed by hiring managers as an AI/ML Engineer, you need to show you can build real systems, not just train models. The best way to learn this is by working on projects that use free public APIs. These projects make you handle real-world data, rate limits, and network issues; the same challenges you’ll face at work. In this article, I’ll share my top 10 AI/ML projects using free public APIs that you should try.
AI/ML Projects Using Free APIs
Here are 10 AI/ML projects with free public APIs that help you move from theory to real-world applications:
- Build an AI Code Review Bot for GitHub
- Building LLM Based MCP Server
- Build a Production-Ready LLM API
- AI System to Summarize YouTube Videos into Notes
- Music Recommendation System Using Spotify API
- YouTube Video Chaptering (YouTube API + NLP)
- Building a Multi-Agent System using Gemini API
- Deploy Your First ML Model as a REST API
- Deploy a Machine Learning Model with Docker
- Code Generation Model using LLMs (GitHub API + Fine-Tuning)
Closing Thoughts
That’s my list of 10 AI/ML projects using free public APIs that I recommend you try building.
As you work on these projects, you’ll run into challenges. Sometimes the API will give you a 429 Too Many Requests error. Your Docker container might not build because of a dependency issue. Your LLM could return a JSON string that breaks your app. Handling these real-world problems is exactly what companies want you to be able to do.
If you found this article helpful, you can follow me on Instagram for daily AI tips and practical resources. You may also be interested in my latest book, Hands-On GenAI, LLMs & AI Agents, a step-by-step guide to prepare you for careers in today’s AI industry.
