Cosa imparerai
- Project 1: Make AI-powered brochure generator that scrapes and navigates company websites intelligently.
- Project 2: Build Multi-modal customer support agent for an airline with UI and function-calling.
- Project 3: Develop Tool that creates meeting minutes and action items from audio using both open- and closed-source models.
- Project 4: Make AI that converts Python code to optimized C++, boosting performance by 60,000x!
- Project 5: Build AI knowledge-worker using RAG to become an expert on all company-related matters.
- Project 6: Capstone Part A – Predict product prices from short descriptions using Frontier models.
- Project 7: Capstone Part B – Execute Fine-tuned open-source model to compete with Frontier in price prediction.
- Project 8: Capstone Part C – Build Autonomous multi agent system collaborating with models to spot deals and notify you of special bargains.
- Compare and contrast the latest techniques for improving the performance of your LLM solution, such as RAG, fine-tuning and agentic workflows
- Weigh up the leading 10 frontier and 10 open-source LLMs, and be able to select the best choice for a given task
Requisiti
- Familiarity with Python. This course will not cover Python basics and is completed in Python.
- A PC with an internet connection is required. Either Mac (Linux) or Windows.
- We recommend that you allocate around $5 for API costs to work with frontier models. However, you can complete the course using open-source models if you prefer.
Descrizione
Mastering Generative AI and LLMs: An 8-Week Hands-On Journey
Accelerate your career in AI with practical, real-world projects led by industry veteran Ed Donner. Build advanced Generative AI products, experiment with over 20 groundbreaking models, and master state-of-the-art techniques like RAG, QLoRA, and Agents.
What you’ll learn
• Build advanced Generative AI products using cutting-edge models and frameworks.
• Experiment with over 20 groundbreaking AI models, including Frontier and Open-Source models.
• Develop proficiency with platforms like HuggingFace, LangChain, and Gradio.
• Implement state-of-the-art techniques such as RAG (Retrieval-Augmented Generation), QLoRA fine-tuning, and Agents.
• Create real-world AI applications, including:
• A multi-modal customer support assistant that interacts with text, sound, and images.
• An AI knowledge worker that can answer any question about a company based on its shared drive.
• An AI programmer that optimizes software, achieving performance improvements of over 60,000 times.
• An ecommerce application that accurately predicts prices of unseen products.
• Transition from inference to training, fine-tuning both Frontier and Open-Source models.
• Deploy AI products to production with polished user interfaces and advanced capabilities.
• Level up your AI and LLM engineering skills to be at the forefront of the industry.
About the Instructor
I’m Ed Donner, an entrepreneur and leader in AI and technology with over 20 years of experience. I’ve co-founded and sold my own AI startup, started a second one, and led teams in top-tier financial institutions and startups around the world. I’m passionate about bringing others into this exciting field and helping them become experts at the forefront of the industry.
Projects:
Project 1: AI-powered brochure generator that scrapes and navigates company websites intelligently.
Project 2: Multi-modal customer support agent for an airline with UI and function-calling.
Project 3: Tool that creates meeting minutes and action items from audio using both open- and closed-source models.
Project 4: AI that converts Python code to optimized C++, boosting performance by 60,000x!
Project 5: AI knowledge-worker using RAG to become an expert on all company-related matters.
Project 6: Capstone Part A – Predict product prices from short descriptions using Frontier models.
Project 7: Capstone Part B – Fine-tuned open-source model to compete with Frontier in price prediction.
Project 8: Capstone Part C – Autonomous agent system collaborating with models to spot deals and notify you of special bargains.
Why This Course?
• Hands-On Learning: The best way to learn is by doing. You’ll engage in practical exercises, building real-world AI applications that deliver stunning results.
• Cutting-Edge Techniques: Stay ahead of the curve by learning the latest frameworks and techniques, including RAG, QLoRA, and Agents.
• Accessible Content: Designed for learners at all levels. Step-by-step instructions, practical exercises, cheat sheets, and plenty of resources are provided.
• No Advanced Math Required: The course focuses on practical application. No calculus or linear algebra is needed to master LLM engineering.
Course Structure
Week 1: Foundations and First Projects
• Dive into the fundamentals of Transformers.
• Experiment with six leading Frontier Models.
• Build your first business Gen AI product that scrapes the web, makes decisions, and creates formatted sales brochures.
Week 2: Frontier APIs and Customer Service Chatbots
• Explore Frontier APIs and interact with three leading models.
• Develop a customer service chatbot with a sharp UI that can interact with text, images, audio, and utilize tools or agents.
Week 3: Embracing Open-Source Models
• Discover the world of Open-Source models using HuggingFace.
• Tackle 10 common Gen AI use cases, from translation to image generation.
• Build a product to generate meeting minutes and action items from recordings.
Week 4: LLM Selection and Code Generation
• Understand the differences between LLMs and how to select the best one for your business tasks.
• Use LLMs to generate code and build a product that translates code from Python to C++, achieving performance improvements of over 60,000 times.
Week 5: Retrieval-Augmented Generation (RAG)
• Master RAG to improve the accuracy of your solutions.
• Become proficient with vector embeddings and explore vectors in popular open-source vector datastores.
• Build a full business solution similar to real products on the market today.
Week 6: Transitioning to Training
• Move from inference to training.
• Fine-tune a Frontier model to solve a real business problem.
• Build your own specialized model, marking a significant milestone in your AI journey.
Week 7: Advanced Training Techniques
• Dive into advanced training techniques like QLoRA fine-tuning.
• Train an open-source model to outperform Frontier models for specific tasks.
• Tackle challenging projects that push your skills to the next level.
Week 8: Deployment and Finalization
• Deploy your commercial product to production with a polished UI.
• Enhance capabilities using Agents.
• Deliver your first productionized, agentized, fine-tuned LLM model.
• Celebrate your mastery of AI and LLM engineering, ready for a new phase in your career.
A chi è rivolto questo corso:
- Aspiring AI engineers and data scientists eager to break into the field of Generative AI and LLMs.
- Professionals looking to upskill and stay competitive in the rapidly evolving AI landscape.
- Developers interested in building advanced AI applications with practical, hands-on experience.
- Individuals seeking a career transition or aiming to enhance productivity through LLM-built frameworks.
Insegnanti
- Valutazione dell'insegnante: 4,6
- 998.882 recensioni
- 3.482.743 studenti
- 121 corsi
Join 3.4M+ learners who study with Ligency.
With a 4.6 instructor rating, ~1M reviews, and 121 courses in 12 languages, we help engineers, leaders, and teams master the skills that power today’s AI revolution - then ship real results.
We start where the real world starts: with large language models and the products they power. You’ll learn the foundations of AI and Generative AI (gen AI), then ship production-grade systems - chatbots, copilots, automations, and AI agents. We go deep on LLM engineering: retrieval (RAG), evaluation, observability, safety, and the patterns teams use to run agentic systems at scale.
Our stack is practical and current. You’ll prototype fast with Python, LangChain, and LangGraph; explore models from OpenAI, Gemini, and Claude (including Claude Code); fine-tune and serve with Hugging Face and Ollama; and take it to production on AWS - from Bedrock to event-driven services. Need automation? We wire it together with n8n, clean interfaces, and CI/CD. Along the way you’ll master prompt engineering that holds up under load.
Where this leads: roles that ship. AI Engineer and LLM Engineer for those who love building; platform and MLOps paths for those drawn to reliability at scale; product and leadership tracks for the people moving Agentic AI from slide decks to business outcomes. The through-line is the same: learn fast, build faster, measure everything, iterate.
Start with our best selling course:
LLM Engineering: Master AI, Large Language Models & Agents - a hands-on path from first prompt to production patterns (RAG, agents, evaluation, LangChain/LangGraph).
The Complete Agentic AI Engineering Course - design, orchestrate, and deploy robust AI agents and agentic workflows.
AI in Production: Gen AI and Agentic AI at Scale - scaling patterns for pipelines, monitoring, and enterprise rollout on AWS.
Generative AI Executive Briefing: LLMs for Leaders - a concise playbook for strategy, governance, and ROI in Generative AI.
If your goal is to level up quickly and ship something real, join us. Learn the concepts, touch the tools, build the thing - then take it to users. That’s the Ligency way.
- Valutazione dell'insegnante: 4,7
- 38.621 recensioni
- 248.380 studenti
- 9 corsi
Ed Donner is a technology leader and repeat founder of AI startups. He’s the co-founder and CTO of Nebula, the platform to source, understand, engage and manage talent, using Generative AI and other forms of machine learning. Nebula’s long-term goal is to help people discover their potential and pursue their reason for being.
Previously, Ed was the founder and CEO of AI startup untapt, an Accenture Fintech Innovation Lab company, acquired in 2021. Before that, Ed was a Managing Director at JPMorgan Chase, leading a team of 300 software engineers in Risk Technology across 3 continents, after a 15-year technology career on Wall Street. Ed holds a patent for a Deep Learning matching engine issued in 2023, and an MA in Physics from Oxford.