Description
In this course, you will :
- Project 1: Create an AI-powered brochure generator that crawls and navigates company websites intelligently.
- Project 2: Create a multi-modal customer care agent for an airline using UI and function-calling.
- Project 3: Create a tool that generates meeting minutes and action items from audio using both open and closed-source models.
- Project 4: Create AI that translates Python code to efficient C++, increasing efficiency by 60,000 times!
- Project 5: Create an AI knowledge worker utilizing RAG to become an expert on all company-related issues.
- Project 6: Capstone Part A: Use Frontier models to predict product pricing based on short descriptions.
- Project 7: Capstone Part B - Implement a fine-tuned open-source model to compete with Frontier in price prediction.
- Project 8: Capstone Part C - Create an autonomous multi-agent system that collaborates with models to identify deals and alert you of special offers.
- Design and implement a comprehensive solution to a given business challenge by recruiting, training, and deploying LLMs.
- Compare and contrast the most recent strategies for optimizing the performance of your LLM solution, including RAG, fine-tuning, and agentic workflows.
- Weigh the top 10 frontier and open-source LLMs and be able to choose the best option for a specific assignment.
Syllabus:
- Week 1 - Build Your First LLM Product: Exploring Top Models & Transformers
- Week 2 - Build a Multi-Modal Chatbot: LLMs, Gradio UI, and Agents in Action
- Week 3 - Open-Source Gen AI: Building Automated Solutions with HuggingFace
- Week 4 - LLM Showdown: Evaluating Models for Code Generation & Business Tasks
- Mastering RAG: Build Advanced Solutions with Vector Embeddings & LangChain
- Week 6: Fine-tuning Frontier Large Language Models with LoRA/QLoRA
- Fine-tuned open-source model to compete with Frontier in price prediction
- Week 8 - Build Autonomous multi agent system collaborating with models