Ollama Zero to Hero: Build Chat, Vision Games & AI Agents

Master Local LLMs: Build 4 AI Projects with Python – From Simple Chat to Smart Agents & RAG Systems.

What you’ll learn
Install and configure Ollama on any operating system (including Docker) and troubleshoot common installation issues
Build custom language models using Modelfiles, including setting up system prompts and optimizing parameters for specific use cases
Implement Ollama’s REST API to create interactive applications, including handling streaming responses and managing conversation context
Design and implement production-ready applications using Ollama, incorporating security best practices and error handling
Optimize model performance through effective memory management, caching, and resource monitoring techniques
Integrate Ollama with popular frameworks like LangChain and LlamaIndex to build advanced AI applications
Deploy Retrieval-Augmented Generation (RAG) systems using Ollama, including vector storage integration and query optimization
Analyze and resolve performance bottlenecks in Ollama deployments using monitoring tools and optimization strategies
Run Ollama APIs using Postman
Run Ollama framework inside CrewAI to build AI Agents powered by Local LLMs

Requirements
Some understanding of Python is preferred but not necessary.

Description
Mastering Ollama: Build Production-Ready AI Applications with Local LLMsTransform your AI development skills with this comprehensive, hands-on course on Ollama – your gateway to running powerful language models locally. In this practical course, you’ll learn everything from basic setup to building advanced AI applications, with 95% of the content focused on real-world implementation.Why This Course?The AI landscape is rapidly evolving, and the ability to run language models locally has become crucial for developers and organizations. Ollama makes this possible, and this course will show you exactly how to leverage its full potential.What Makes This Course Different?✓ 95% Hands-on Learning: Less theory, more practice ✓ Real-world Projects: Build actual applications you can use ✓ Latest Models: Work with cutting-edge LLMs like Llama 3.2, Gemma 2, and more ✓ Production-Ready Code: Learn best practices for deployment ✓ Complete AI Stack: From basic chat to advanced RAG systemsCourse JourneySection 1: Foundations of Local LLMsStart your journey by understanding why local LLMs matter. You’ll learn:What makes Ollama unique in the LLM landscapeHow to install and configure Ollama on any operating systemBasic operations and model managementYour first interaction with local language modelsSection 2: Building with PythonGet hands-on with the Ollama Python library:Complete Python API walkthroughBuilding conversational interfacesHandling streaming responsesError management and best practicesPractical exercises with real-world applicationsSection 3: Advanced Vision ApplicationsCreate exciting visual AI applications:Working with Llama 2 Vision modelsBuilding an interactive vision-based gameImage analysis and generationMulti-modal applicationsPerformance optimization techniquesSection 4: RAG Systems & Knowledge BasesImplement production-grade RAG systems:Setting up Nomic embeddingsVector database integrationWorking with Gemma 2 modelQuery optimizationContext window managementReal-time document processingSection 5: AI Agents & AutomationBuild intelligent agents using state-of-the-art models:Architecting AI agents with Gemma 2Task planning and executionMemory managementTool integrationMulti-agent systemsPractical automation examplesPractical Projects You’ll BuildInteractive Chat ApplicationBuild a real-time chat interfaceImplement context managementHandle streaming responsesDeploy as a web applicationVision-Based GameCreate an interactive game using Llama 2 VisionImplement real-time image processingBuild engaging user interfacesOptimize performanceEnterprise RAG SystemDevelop a complete document processing systemImplement efficient vector searchCreate intelligent query processingBuild a production-ready APIIntelligent AI AgentBuild an autonomous agent using Gemma 2Implement task planning and executionCreate tool integration frameworkDeploy for real-world automationWhat You’ll LearnBy the end of this course, you’ll be able to:Set up and optimize Ollama for production useBuild complex applications using various LLM modelsImplement vision-based AI solutionsCreate production-grade RAG systemsDevelop intelligent AI agentsDeploy and scale your AI applicationsWho Should Take This Course?This course is perfect for:Software developers wanting to integrate AI capabilitiesML engineers moving to local LLM deploymentsTechnical leaders evaluating AI infrastructureDevOps professionals managing AI systemsPrerequisitesTo get the most out of this course, you should have:Basic Python programming experienceFamiliarity with REST APIsUnderstanding of command-line operationsComputer with minimum 16GB RAM (32GB recommended)Why Learn Ollama?Cost-effective: Run models locally without API costsPrivacy-focused: Keep sensitive data within your infrastructureCustomizable: Modify models for your specific needsProduction-ready: Build scalable, enterprise-grade solutionsCourse Format95% hands-on practical contentStep-by-step project buildsReal-world code examplesInteractive exercisesProduction-ready templatesBest practice guidelinesSupport and ResourcesComplete source code for all projectsProduction-ready templatesTroubleshooting guidesPerformance optimization tipsDeployment checklistsCommunity supportJoin us on this exciting journey into the world of local AI development. Transform from a regular developer into an AI engineering expert, capable of building and deploying sophisticated AI applications using Ollama.Start building production-ready AI applications today!

Who this course is for
Software Developers & Engineers
ML/AI Engineers
Technical Team Leaders
DevOps Professionals
Business Leaders
Entrepreneurs

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