Lessons
- Intro
- What Is this course about?
- Course Prerequisites
- Development Environment Setup
- Ollama Deep Dive
- Ollama Key Features
- Ollama Setup
- Download Ollama Locally
- Ollama Models - How to Pull Different Ollama Models Locally
- LLM Parameters Deep Dive
- Understanding Model Benchmarks
- Ollama Basic CLI Commands - Pull and Testing Models
- Pull in the Llava Multimodal MOdel and Captioning an Image - Hands-on
- Summarize and Sentiment Analysis and Customizing Models with the Modelfile
- Ollama REST API
- Ollama REST API - Request JSON
- Ollama Models Support Different Tasks - Summary
- Different Ways to Interact with Ollama Models - Overview
- Ollama Model Running Under Msty App - Frontend Tool - RAG Hands-on
- Introduction to Python Library for Building LLM Applications Locally
- Interact with Llama3 in Python using Ollama REST API
- Ollama Python Library Chatting with our Model
- Chat Example with Streaming
- Using Ollama Show Function
- Create a Custom Model in Code
- Build a Real-world Use case Application - Introduction
- Build a LLM App - Grocery List Organizer
- Building RAG Systems with Ollama - Overview of RAG Systems and Langchain Crash Course
- Deep Dive into Vectorstore and Embeddings - the Whole Picture - Crash Course
- Overview of Our PDF RAG System We will be Building
- Set up our RAG System - Document Ingestion and Vector DB Creation and Embeddings
- RAG System - Retrieval and Querying - Final
- RAG System - Cleaner Code (Code Refactoring)
- RAG System - Streamlit Version
- BONUS for YOU!
- Introduction to the Next Application - AI Recruiter Agency
- Building the AI Recruiter Agency
- Outro - Final Thoughts and Your Bonus - Thank you!
Learning panel
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