Large Language Models (LLMs) are at the core of today’s AI revolution, powering chatbots, automation systems, and intelligent applications. However, deploying and customizing them often feels complex and cloud-dependent. Ollama changes that by making it easy to run, manage, and fine-tune LLMs locally on your machine.

This course is designed for developers, AI enthusiasts, and professionals who want to master LLMs on their own hardware/laptop using Ollama. You’ll learn everything from setting up your environment to building custom AI models, fine-tuning them, and integrating them into real applications, all without relying on expensive cloud infrastructure.

What’s in this course?

We start with the fundamentals of LLMs and Ollama, explore their architecture, and understand how Ollama compares with tools like LangChain and Hugging Face. From there, you’ll set up Ollama across different operating systems, work with its CLI and desktop tools, and dive deep into model creation and management.

You will build practical projects, including:

• Creating and configuring custom AI models using Modelfile

• Integrating Ollama with Python, REST APIs, and Streamlit

• Fine-tuning models with custom datasets (CSV/JSON)

• Managing multiple versions of fine-tuned models

• Building your first local RAG (Retrieval-Augmented Generation) app with Ollama

By the end, you’ll be fully equipped to deploy and run advanced LLM applications locally, giving you full control, privacy, and flexibility.

Special Note

This course emphasizes hands-on, practical learning. Every module includes live demonstrations with real-world troubleshooting, so you gain not just the theory but also the confidence to implement LLM solutions independently.

Course Structure

• Lectures

• Live Demonstrations

Course Contents

• Introduction to LLMs and Ollama

• Architecture of Ollama

• Comparison - Ollama vs LangChain vs Hugging Face

• Setting Up Ollama Environment

• Commonly used Ollama Commands (CLI)

• Understanding Model Configuration file (Modelfile)

• Working with Models (Configuration, Registry, Tokens, Context length)

• Ollama with Python (REST API, Python Library, Streamlit UI)

• Model Fine-tuning and Version Management

• Building Your First Local RAG App

All sections include hands-on demonstrations. Learners are encouraged to set up their own Ollama environments, follow along with the exercises, and reinforce their understanding through practical approach.