Udemy – LLM Mastery: From Raw Data to Running Model – How They WORK! 2025-7

Udemy – LLM Mastery: From Raw Data to Running Model – How They WORK! 2025-7 Downloadly IRSpace

Aug 17, 2025 - 02:42 Updated: Aug 19, 2025 - 14:37

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Udemy – LLM Mastery: From Raw Data to Running Model – How They WORK! 2025-7

LLM Mastery: From Raw Data to Running Model – How They WORK!. This course is a comprehensive guide to understanding and implementing large language models (LLMs) from theory to production implementation. Rather than providing simplified concepts, this course provides a detailed and practical demonstration of how LLMs work in the real world, covering all stages of their lifecycle, from raw data collection to final model deployment. Participants will learn basic concepts such as tokenization, Transformer architecture, and Attention mechanisms, and then work through the design and implementation of three types of LLMs for a variety of applications such as coding, creative writing, and data analysis. The course also covers advanced topics such as initial training, fine-tuning, quantization, and model deployment with tools such as KoboldCPP and GGUF files. The limitations and ethical challenges of LLMs are also discussed through real-world case studies. This course helps participants gain a deep understanding of architectural decisions, the differences between different models, and how to optimize them for practical applications.

What you will learn

Everything about LLMs: from the place of LLMs in AI, their history and evolution, the importance of big data, to key components like Tokenization and Transformer architecture.
Architecture and Design of LLMs: You will design three types of LLMs with different purposes.
Understand the basic concepts of LLMs: You will understand the architecture of ChatGPT, GPT-4, and Claude and how LLMs work.
Build and deploy LLMs locally: You run AI models offline with KoboldCPP, GGUF files, and the Quantization technique.
Deep understanding of Transformer architecture: You will learn the Attention and Tokenization mechanisms that are the core of modern LLMs like GPT and BERT.
Learning to Teach, Fine-Tuning, and Prompt Engineering: From Pre-Training to Deployment in the Real Environment.
LLM Architecture Design: For coding (such as GitHub Copilot), creative writing, and research applications.
Mastering sampling parameters and methods: Learning Temperature settings for optimal text generation by AI.
Implementing practical applications of LLM: building chatbots, generating code, and creating content through practical projects.
Understanding the limitations, biases, ethics and safety of LLM: Learning practices for responsible development of AI.

This course is suitable for people who:

Complete beginners: People who want to understand LLMs, ChatGPT, Claude, and modern AI language models.
Developers, data scientists, and engineers: professionals looking to enter the field of LLM development and AI applications.
Business professionals, product managers, and entrepreneurs: People who plan to build AI-based products using LLMs.
Students and researchers: People studying natural language processing, Transformers, and deep learning.
Content creators, writers, and marketers: Those who use LLMs for automation and AI-powered workflows.

Course Details LLM Mastery: From Raw Data to Running Model – How They WORK!

Publisher: Udemy
Instructor: Cloudy Robotics
Training level: Beginner to advanced
Training duration: 3 hours and 36 minutes
Number of lessons: 31

Course topics

Prerequisites for the LLM Mastery: From Raw Data to Running Model – How They WORK!

No Prior Coding Experience Required:
An interest in natural language processing and how machines understand and generate human language.
A computer with a stable internet connection to access course materials and complete online projects.
Optional: A basic understanding of artificial intelligence and machine learning concepts will be helpful but is not mandatory. The course will cover necessary foundational topics.
Optional: Basic Neural Network knowledge
“No coding required – learn LLMs from scratch with beginner-friendly explanations
Basic computer skills – if you can browse the web, you can master Large Language Models
Curiosity about AI, ChatGPT, or natural language processing – we’ll teach everything else
Computer with 8GB RAM minimum for running local LLMs (optional – can follow along without)