🎓 AI and Data Science Student at ENIAD     🤖 Machine Learning · Deep Learning · LLM & RAG Systems     📚 Currently mastering LangChain · LangGraph · Agentic AI

• 🎓 Student at ENIAD — AI & Data Science
• 🤖 Passionate about Machine Learning, Deep Learning & LLM Applications
• 🔗 Building RAG pipelines with LangChain
• 💡 I enjoy turning ideas into real, practical AI projects

Concepts mastered:

• Classical ML
• Deep Learning: CNNs, RNNs, LSTMs, Transformers, BERT, GPT
• Transfer Learning, Fine-tuning)
• Computer Vision: Object Detection, Segmentation, ResNet

• LangChain LCEL — chains, memory, retrieval, agents
• RAG Pipelines — document loading, splitting, embeddings, vector search
• Ollama — local LLMs (Llama 3.2) + embeddings (Qwen3-Embedding)
• ChromaDB — vector store with MMR retrieval
• Embeddings — Qwen3-Embedding, nomic-embed-text

• Complete RAG system built with LangChain LCEL (modern syntax)
• PDF loading → text splitting → embeddings → ChromaDB → LLM response
• Stack: LangChain · Ollama (Llama 3.2) · Qwen3-Embedding · ChromaDB
• Features: MMR retrieval, cosine similarity, persistent vector store
• 100% local & free — no OpenAI API needed

🔗 https://github.com/iliaselamrani212/rag-pipeline-langchain-ollama

• Kaggle Playground Series project
• Data preprocessing, feature engineering, model training
• Algorithms: XGBoost, CatBoost, LightGBM, Ensemble methods

🔗 https://github.com/iliaselamrani212/Diabetes-Prediction

• Machine learning regression project (ASHRAE dataset)
• Time series analysis & feature engineering

🔗 https://github.com/iliaselamrani212/electricity-prediction-ml

• Collaborative Flutter application (3 developers)
• Role: Flutter Developer

🔗 https://github.com/AmineElAtrache/smart_library

✅ LangChain LCEL          → chains, memory, RAG, agents
✅ RAG Systems             → embeddings, vector DB, retrieval
🔄 LangGraph               → agentic AI, multi-agent systems
⏳ LangSmith               → monitoring & evaluation

⭐ Feel free to explore my repositories and connect with me!