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Master the entire lifecycle: Python, Data Science, Machine Learning (XGBoost), Deep Learning (PyTorch, TensorFlow), MLOps (Docker, FastAPI, AWS), and Generative AI (LLMs, RAG, LangChain, CrewAI). Build, deploy, and scale enterprise-ready AI models.\n\n\nDescription\nThis course contains the use of artificial intelligence(AI).\n\nWelcome to Full-Stack AI Engineer: Python, ML, Deep Learning & GenAI, the ultimate end-to-end program designed to turn you into a production-ready Artificial Intelligence Engineer. In this comprehensive AI course, you will master every layer of the AI engineering pipeline, from Python programming and data science foundations to machine learning, deep learning, Recursive Language Models, MLOps, and Generative AI with Large Language Models (LLMs).\n\nThis course is your complete roadmap to becoming a Full-Stack AI Engineer, capable of designing, building, training, deploying, and scaling AI models across real-world environments. 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You’ll learn version control with Git and DVC, model packaging with ONNX and TorchScript, API serving using Flask and FastAPI, and cloud deployment on AWS, GCP, and Azure. You’ll automate model pipelines using CI/CD tools, ensuring that your models are reliable, scalable, and ready for enterprise use.\n\nFinally, you’ll dive into Generative AI (GenAI) and Large Language Models (LLMs). You’ll master prompt engineering, tokenization, fine-tuning, retrieval-augmented generation (RAG), and AI agent frameworks like LangChain and CrewAI. You’ll build real LLM applications using OpenAI GPT, Claude, and Gemini APIs, culminating in a capstone project where you develop your own AI chatbot or content generator.\n\nBy the end of this course, you’ll have the full technical stack to become a Full-Stack AI Engineer — a professional who understands data science, machine learning, deep learning, MLOps, and Generative AI end-to-end. 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