In this course, you will learn how to create local language models using Ollama and Python. By the end, you will be equipped with the tools to build LLM-based applications for real-world use cases. The course introduces Ollama's powerful features, installation, and setup, followed by a hands-on guide to exploring and utilizing Ollama models through Python. You'll dive into topics such as REST APIs, the Python library for Ollama, and how to customize and interact with models effectively.

You'll begin by setting up your development environment, followed by an introduction to Ollama, its key features, and system requirements. After grasping the fundamentals, you'll start working with Ollama CLI commands and explore the REST API for interacting with models. The course provides practical exercises such as pulling and testing models, customizing them, and using various endpoints for tasks like sentiment analysis and summarization. The journey continues as you dive into Python integration, using the Ollama Python library to build LLM-based applications. You'll explore advanced features like working with multimodal models, creating custom models, and using the show function to stream chat interactions. Then, you'll develop full-fledged applications, such as a grocery list categorizer and a RAG system, exploring vector stores, embeddings, and more. This course is ideal for those looking to build advanced LLM applications using Ollama and Python. If you have a background in Python programming and want to create sophisticated language-based applications, this course will help you achieve that goal. Expect a hands-on learning experience with the opportunity to work on several projects using the Ollama framework.