AI Projects
Build real AI applications from scratch. Step-by-step projects with full working code — from RAG chatbots to fraud detectors, voice assistants to MLOps pipelines.
All Projects
20 hands-on projects to build real AI applications from scratch.
LLM & GenAI Projects
Build a RAG Chatbot
Build a retrieval-augmented generation chatbot that answers questions using your own documents and knowledge base.
8 LessonsBuild a Voice Assistant
Create a voice-powered AI assistant with speech recognition, natural language understanding, and text-to-speech.
7 LessonsBuild an AI Writing Assistant
Build an intelligent writing tool that helps with drafting, editing, tone adjustment, and content generation.
7 LessonsBuild an AI Email Assistant
Create an AI-powered email assistant that drafts replies, summarizes threads, and prioritizes your inbox.
7 LessonsBuild a Multi-Agent Workflow
Design and implement a multi-agent system where specialized AI agents collaborate to solve complex tasks.
7 LessonsML & Data Projects
Build a Recommendation Engine
Build a recommendation system using collaborative filtering, content-based methods, and hybrid approaches.
8 LessonsBuild a Fraud Detector
Create a real-time fraud detection system using anomaly detection, feature engineering, and ML classification.
8 LessonsBuild a Stock Predictor
Build a stock price prediction model using time series analysis, LSTM networks, and market feature engineering.
8 LessonsBuild a Computer Vision App
Create a computer vision application with image classification, object detection, and visual analysis pipelines.
7 LessonsPlatform & Infrastructure
Build an AI Search Engine
Build a semantic search engine using vector embeddings, similarity search, and intelligent ranking algorithms.
8 LessonsBuild an AI API Gateway
Create an API gateway for AI services with rate limiting, load balancing, model routing, and monitoring.
7 LessonsBuild an ML Feature Platform
Build a feature store and platform for managing, versioning, and serving ML features at scale.
7 LessonsBuild an MLOps Pipeline
Create an end-to-end MLOps pipeline with CI/CD, model versioning, monitoring, and automated retraining.
8 LessonsBuild an AI Dashboard
Build an interactive dashboard for visualizing AI model performance, metrics, and real-time predictions.
7 LessonsApplication Projects
Build an AI Code Review Tool
Create an AI-powered code review tool that analyzes code quality, finds bugs, and suggests improvements.
7 LessonsBuild an AI Image Generator
Build an image generation application using diffusion models, prompt engineering, and image processing pipelines.
7 LessonsBuild a Document Intelligence App
Create a document processing system with OCR, entity extraction, classification, and intelligent summarization.
7 LessonsBuild an AI Content Moderator
Build an automated content moderation system using NLP, image analysis, and policy enforcement rules.
7 LessonsBuild an AI Customer Support Bot
Create an intelligent customer support chatbot with intent detection, knowledge retrieval, and escalation logic.
7 LessonsBuild an AI Resume Screener
Build an AI-powered resume screening tool with skill extraction, candidate matching, and ranking algorithms.
7 LessonsWhat You'll Build
Skills and capabilities you will gain across these 20 projects.
LLM-Powered Applications
Build production-ready applications using large language models, retrieval-augmented generation, and multi-agent architectures with full working code.
ML Systems & Pipelines
Create end-to-end machine learning systems including recommendation engines, fraud detectors, and predictive models with proper feature engineering.
AI Infrastructure
Design and deploy AI platform components like API gateways, feature stores, MLOps pipelines, and monitoring dashboards for production use.
Real-World AI Tools
Ship practical AI tools for code review, content moderation, document intelligence, and customer support that solve actual business problems.
AI Projects is the hands-on, build-something track. Each project in the track is a runnable end-to-end system (a RAG chatbot, a fraud detector, a voice assistant, a recommendation engine, an MLOps pipeline, an agent, a semantic search system, a fine-tuning workflow) with real code, real data, and the production considerations that usually get skipped in tutorial content. The projects are chosen to each teach something a reader will carry into their next real project.
We chose the projects deliberately to cover the spectrum of production AI: classical ML, LLM applications, agents, retrieval systems, evaluation and testing, and deployment. The code is written to be read as reference architecture, not copy-pasted verbatim. Each project ends with the honest tradeoffs we encountered (where it would not scale, where we cut corners, what we would change for a 10x use case) so you can ship with a clear view of what you inherited.