A collection of small AI experiments — text summarization, image captioning, and creative mini-projects using OpenAI models. Learn, test, and document new ideas in applied machine intelligence.

A personal lab for experimenting with mini LLMs, transformers, agents, and applied AI while learning and building AI systems from scratch. 🧠⚙️

The AI Lab Aiming to create a fresh, hands-on space for learning and experimenting with AI. This new repository features practical tutorials, clear examples, and mini-projects covering machine learning, deep learning, and more. Join the lab to explore and grow your AI skills step by step.

AI Engineering Learning Lab. This repository documents my daily journey of studying and implementing machine learning concepts in Python. Each topic includes theory notes, practical implementations, experiments, and mini projects using NumPy, Pandas, and Scikit-learn.

🧠 genai-rag-playground A personal learning lab for experimenting with Generative AI, RAG (Retrieval-Augmented Generation), and Streamlit. This repository contains multiple mini-projects and prototypes that explore how to build intelligent applications using Large Language Models (LLMs), vector databases, document retrieval, and modern UIs.

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**AI Engineer Systems Lab** — a hands-on collection of mini projects and experiments inspired by O’Reilly’s *AI Engineer*, showcasing end-to-end AI system design, deployment, and research-driven applications.

Self-driven LLM engineering lab with concepts, experiments, and mini-projects built from first principles while learning embeddings, prompting, RAG, and real-world AI workflows.

My personal project and learning collection Projects and resources related to web dev, ML & React Developer log – React, Node.js, and AI experiments Code lab – full-stack apps, mini projects & more

🚀 A personal lab for mastering the next-generation full-stack ecosystem — Next.js, React, TypeScript, Prisma, PostgreSQL, and AI integration with OpenAI & LangChain. This repo documents my learning journey, experiments, and mini projects toward becoming a full-stack + AI engineer.

Personal lab for experimenting with AI agents and multi-agent systems. Mini-projects exploring different frameworks (CrewAI, LangGraph, AutoGen, OpenAI SDK) and patterns like RAG, tool-calling, and autonomous workflows.

In AI & Data Science, lab experiments and mini projects focus on applying theoretical concepts through practical implementation of algorithms, data analysis, and machine learning models using tools like Python. These activities help students gain hands-on experience, improve problem-solving skills, and develop real-world intelligent solutions.