My learning journey and projects from Ed Donner’s LLM Engineering course

Welcome to From Zero to LLM, a journey into the fascinating world of Large Language Models (LLMs). This repository serves as a personal learning diary, showcasing hands-on experiments, tutorials, and projects related to LLMs, including fine-tuning, prompt engineering, and real-world applications.

"📚 My daily journey mastering AI: step-by-step progress through LLMs, RAG systems, prompt engineering, and agent development. Learning something new about AI every single day."

AI Engineering learning journey documenting the intersection of UX/Product Strategy and AI development. Mapping existing capabilities, active learning (LLMs, RAG, agents), and skillset progression. Addressing the gap where technical execution meets product thinking and usability.

My learning journey and projects from Ed Donner’s LLM Engineering course

Projects I implemented during my journey of learning LLM Engineering

Structured archive of my AI engineering learning journey: from data science to LLM and infrastructure.

My personal journey learning AI engineering, machine learning, LLMs, and RAG through hands-on practice and course exercises.

My journey through learning how to work with LLMs and agents via Ed Donner's LLM Engineering course on Udemy

Personal repository documenting my journey to understand and implement AI systems from first principles. Covers mathematics, machine learning, deep learning, transformers, and LLM engineering, with notes, experiments, and implementations.

Learning journey through Ed Donner’s LLM Engineering: Master AI and Large Language Models. This repo contains notes, code experiments, and hands-on projects covering the foundations and applications of LLMs, including prompt engineering, fine-tuning, RAG, and deployment.

Generative AI Learning Journey (5 Days) — Hands-on exploration of foundational models, prompt engineering, embeddings, AI agents, domain-specific LLMs, and MLOps for Generative AI using Vertex AI tools.

A collection of assignments, projects, and notes from the AI/LLM Engineering Bootcamp. This repository documents my learning journey in building, evaluating, and deploying language model-powered applications using tools like OpenAI, Python, and prompt engineering techniques.

A collection of my Python work from beginner to intermediate level, focusing on core concepts, problem-solving, and foundations for LLM engineering. This repo documents my learning journey and practical coding practice.

This repository documents my hands-on learning journey in Retrieval-Augmented Generation (RAG), LangChain, and Prompt Engineering. It contains experiments, notes, and small implementations created while understanding how modern LLM-based systems are built.

A structured learning repository documenting my journey in prompt engineering for large language models. It covers core concepts, prompting techniques, real-world patterns, and daily prompt experiments to build practical intuition for working with LLMs.

This repository documentsof my journey data analyst through a self-designed 6-month challenge to master Full Stack Data Science, Generative AI (LLMs, prompt engineering), Agentic AI (autonomous agents), Machine Learning, and AI development with Python.

Personal portfolio showcasing my journey as an AI/ML Engineer — featuring projects in GenAI, LLMs, NLP, Computer Vision, and Machine Learning. Includes deployed apps, code, and hands-on builds demonstrating practical problem-solving and engineering depth.

A personal Generative AI learning lab exploring LLMs and modern GenAI systems using OpenAI, Hugging Face, Ollama, Groq, and Anthropic. The repository covers core concepts, prompt engineering, LangChain workflows, RAG pipelines, and hands-on experiments, documenting a structured journey from fundamentals to practical LLM-powered applications.

This repository contains my learning journey and projects from a 6-month intensive bootcamp on Data Science, Generative AI, and Agentic AI. Includes hands-on assignments, coding, and mini-projects covering topics such as Python, data analysis, machine learning, deep learning, LLMs, prompt engineering, and building intelligent agentic systems.

This repository documents my learning journey in Large Language Models (LLMs) and Generative AI (GenAI). 🌱 What you’ll find here Basics of LLMs: tokens, embeddings, transformers, prompt engineering Explorations in Generative AI: text generation, RAG, fine-tuning (LoRA/PEFT) and more .Projects using frameworks like LangChain,OpenAI API .

A living workspace for my LLM learning journey, assignments, experiments, and practical projects built as I explore modern Large Language Models. Expect continuous updates: prompt engineering, multi-agent systems, LiteLLM pipelines, RAG, evaluation tools, and everything I learn along the way.

This repository reflects my learning journey in Generative AI, inspired by CampusX and Nitish Jain. It includes hands-on implementations of LLMs, chat models, embeddings, and prompt engineering using Python. The project focuses on practical understanding of conversational AI, structured prompts, and context-aware systems.

A living record of my professional journey in mastering Generative AI, featuring RAG systems, LLMs, and Prompt Engineering as I progress through Google Cloud learning paths. سجل متكامل لمساري المهني في احتراف الذكاء الاصطناعي التوليدي، يضم أنظمة RAG ونماذج اللغة الضخمة وهندسة الأوامر، كتوثيق حي لتقدمي في مسارات تعلم جوجل كلاود

A complete hands-on Generative AI learning journey covering the fundamentals of LLMs, LangChain, and prompt engineering — along with real-world AI projects like chatbots, document assistants, search engines, and more. Designed to build end-to-end understanding of how to integrate GenAI models into real-world applications using Python and LangChain.