Build local LLM applications using Python and Ollama

Learn to create LLM applications in your system using Ollama and LangChain in Python | Completely private and secure

What you'll learn
Download and install Ollama for running LLM models on your local machine
Set up and configure the Llama LLM model for local use
Customize LLM models using command-line options to meet specific application needs
Save and deploy modified versions of LLM models in your local environment
Develop Python-based applications that interact with Ollama models securely
Call and integrate models via Ollama's REST API for seamless interaction with external systems
Explore OpenAI compatibility within Ollama to extend the functionality of your models
Build a Retrieval-Augmented Generation (RAG) system to process and query large documents efficiently
Create fully functional LLM applications using LangChain, Ollama, and tools like agents and retrieval systems to answer user queries

Requirements
Basic Python knowledge is recommended, but no prior AI experience is required.

Description
If you are a developer, data scientist, or AI enthusiast who wants to build and run large language models (LLMs) locally on your system, this course is for you. Do you want to harness the power of LLMs without sending your data to the cloud? Are you looking for secure, private solutions that leverage powerful tools like Python, Ollama, and LangChain? This course will show you how to build secure and fully functional LLM applications right on your own machine.In this course, you will:Set up Ollama and download the Llama LLM model for local use.Customize models and save modified versions using command-line tools.Develop Python-based LLM applications with Ollama for total control over your models.Use Ollama's Rest API to integrate models into your applications.Leverage LangChain to build Retrieval-Augmented Generation (RAG) systems for efficient document processing.Create end-to-end LLM applications that answer user questions with precision using the power of LangChain and Ollama.Why build local LLM applications? For one, local applications ensure complete data privacy—your data never leaves your system. Additionally, the flexibility and customization of running models locally means you are in total control, without the need for cloud dependencies.Throughout the course, you'll build, customize, and deploy models using Python, and implement key features like prompt engineering, retrieval techniques, and model integration—all within the comfort of your local setup.What sets this course apart is its focus on privacy, control, and hands-on experience using cutting-edge tools like Ollama and LangChain. By the end, you'll have a fully functioning LLM application and the skills to build secure AI systems on your own.Ready to build your own private LLM applications? Enroll now and get started!

Who this course is for
Software developers who want to build and run private LLM applications on their local machines.
Data scientists looking to integrate advanced LLM models into their workflow without relying on cloud solutions.
Privacy-focused professionals who need to maintain complete control over their data while leveraging powerful AI models.
Tech enthusiasts interested in exploring local LLM setups using cutting-edge tools like Ollama and LangChain.