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From Recipe to Chef: Become an LLM Engineer 100+ Projects

Master Large Language Models with Zero Code! Learn AI, Prompting & Fine-Tuning Through Fun & Tasty Food Analogies(AI)

  • 0.0 (0 Reviews)
  • 1 Students
  • 162 Lessons
  • con chó ăn cứt

Course Overview

From Recipe to Chef: Become an LLM Engineer (Food Analogies) is a fun, beginner-friendly course that teaches you how to master Large Language Models (LLMs) without writing a single line of code. Whether you're curious about AI, looking to break into the world of language models, or want to become an LLM engineer, this course is your gateway to understanding and building with powerful tools like ChatGPT, Claude, Gemini, and LLaMA. We make technical concepts simple and relatable using clever food metaphors—so you can go from kitchen newbie to AI chef in no time.

You'll explore how LLMs are built, trained, deployed, and evaluated through easy-to-understand analogies. Imagine tokenization as chopping vegetables, training as baking at scale, or prompt engineering as seasoning a dish just right. Each module is carefully crafted to introduce a new skill, from data preparation and fine-tuning to evaluation and deployment. By the end, you’ll be fluent in core LLM concepts like model architecture, pretraining, transfer learning, prompt optimization, model evaluation metrics like perplexity and BLEU score, and deploying your own LLM-powered applications using tools like FastAPI, Gradio, Hugging Face Spaces, and LangChain.

This course is perfect for students, educators, creators, entrepreneurs, and professionals from non-technical backgrounds who want to learn AI fundamentals and build real-world applications powered by large language models. We take you step by step through the AI lifecycle—starting from "What is a language model?" all the way to deploying your own chatbot, summarizer, or recommender app. You'll learn to use no-code tools, experiment with real prompts, fine-tune existing models, evaluate outputs, and even explore career paths like prompt engineer, AI product manager, and LLM architect.

No coding experience is required. You’ll learn how to communicate with LLMs using natural language, design smart and effective prompts, and understand what's happening behind the scenes—from data collection and tokenization to the model's prediction process and its computational needs using GPUs and TPUs. You’ll also cover bias detection, hallucinations, feedback loops, and strategies to monitor and improve your AI systems over time.

By the end of the course, you’ll have a solid foundation in LLM theory, a portfolio of hands-on AI projects, and the confidence to step into the growing world of generative AI. Whether you're aiming to build your own AI product, join an AI startup, contribute to open-source projects, or simply impress your friends with your understanding of machine learning concepts, this course will get you there—with a full plate of knowledge and a side of fun.

If you're ready to go from recipe reader to LLM chef, join us on this flavorful journey through the world of large language models, where every concept is explained with relatable metaphors and practical examples.

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Course curriculum

Requirment

  • No programming experience required – this course is designed for absolute beginners.

  • Curiosity about AI and how language models work is more than enough to get started.

  • Basic computer skills like using a browser, uploading files, and typing are helpful.

  • A laptop or desktop with internet access – no fancy hardware needed.

  • Optional: A free OpenAI API key (for hands-on projects using GPT).

  • Optional: Interest in building chatbots, writing prompts, or exploring AI careers.

  • All tools used (like Gradio, Google Colab, or LangChain templates) are free and beginner-friendly.

Outcomes

  • Understand what large language models (LLMs) are and how they work using real-world analogies

  • Identify key ingredients that power LLMs, like training data, tokenization, and data quality.

  • Explain how LLMs are trained using concepts like batches, epochs, and loss functions.

  • Write better prompts using techniques like zero-shot, few-shot, and chain-of-thought.

  • Customize models using fine-tuning and tools like Hugging Face and LoRA.

  • Evaluate model performance using both quantitative and qualitative metrics.

  • Deploy LLMs using APIs, FastAPI/Flask, and host them on platforms like Hugging Face Spaces.

  • Build full LLM-powered applications using no-code tools and LangChain.

  • Monitor and improve your AI models using logs, feedback loops, and A/B testing.

  • Monitor and improve your AI models using logs, feedback loops, and A/B testing.

Instructor

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Con chó ăn cứt

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