Free Download Mastering LLMs Locally using Ollama | Hands-On
Published 8/2025
Created by Yogesh Raheja
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 35 Lectures ( 2h 11m ) | Size: 828 MB
Hands-On Guide to Running, Fine-Tuning, and Integrating LLMs with Ollama
What you'll learn
Fundamentals of LLMs & Ollama
Using Ollama CLI & Desktop
Run open LLMs like Gemma 3, Llama3
Model Registry in Ollama for pushing customized models
Token Count, Context length and Fine-tuning with your own datasets
Ollama with REST API, Ollama-python Library, Integrating Ollama with Python & Streamlit
Model Fine Tuning with Live Demonstration
Building a local RAG application
Requirements
Basic knowledge of Python
Familiarity with AI/ML concepts and LLMs
Interest in working with open-source tools for local AI deployment
Description
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 ModelfileIntegrating Ollama with Python, REST APIs, and StreamlitFine-tuning models with custom datasets (CSV/JSON)Managing multiple versions of fine-tuned modelsBuilding your first local RAG (Retrieval-Augmented Generation) app with OllamaBy the end, you'll be fully equipped to deploy and run advanced LLM applications locally, giving you full control, privacy, and flexibility.Special NoteThis 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 StructureLecturesLive DemonstrationsCourse ContentsIntroduction to LLMs and OllamaArchitecture of OllamaComparison - Ollama vs LangChain vs Hugging FaceSetting Up Ollama EnvironmentCommonly 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 ManagementBuilding Your First Local RAG AppAll 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.
Who this course is for
AI/ML Engineers and Data Scientists
AI/GenAI Enthusiasts looking to run models locally
Tech Leads & Product Managers exploring LLM deployment options
Developers, DevOps, and Cloud Engineers interested in open-source LLM workflows
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