DeepSeek R1: Build AI Agents & RAG Apps on Your Own Machine

Build AI Agents & RAG Apps with DeepSeek R1 vs OpenAI - Run on Ollama & Android, Complete Local Setup Guide

What you'll learn
Deploy DeepSeek R1 locally using Ollama and configure it for optimal performance
Compare DeepSeek R1's capabilities with OpenAI models (O1 and O3) through hands-on experiments
Implement production-ready RAG systems using DeepSeek R1 for custom knowledge bases
Design and develop AI agents using CrewAI framework with DeepSeek R1 as the foundation model
Run DeepSeek R1 efficiently on Android devices for mobile AI applications
Build a complete RAG chatbot system with local PDF processing capabilities
Create multi-agent systems that can handle complex tasks and reasoning
Optimize DeepSeek R1 implementations for production environments

Requirements
Basic understanding of Python
Access to Internet and a Laptop with 16GB RAM

Description
Curious about running powerful AI models on your own machine? DeepSeek R1, the revolutionary open-source model that's challenging OpenAI and Claude, has changed what's possible with local AI. In this hands-on course, you'll learn why this $6M model is making waves in the AI community and how to harness its power for your own projects.Why This Course?Understand why DeepSeek R1 is disrupting the AI industryGet hands-on experience running a powerful LLM locallyBuild practical applications without cloud dependenciesLearn through actual coding, not just theoryWhat Sets This Course Apart: Instead of overwhelming you with complex theory, we focus on practical implementation. You'll start with basic setup and progressively build more sophisticated applications, from simple chat interfaces to advanced RAG systems.Why DeepSeek R1? In a landscape dominated by expensive cloud-based solutions like OpenAI's models, DeepSeek R1 emerges as a game-changing alternative. Learn how this model compares to OpenAI O1 and O3, and discover why it's becoming the go-to choice for developers worldwide.What You'll Learn:Section 1: IntroductionCourse overview and learning pathSetting up your development environmentUnderstanding the AI landscape in 2025Section 2: What is DeepSeek R1?Deep dive into DeepSeek R1's architectureComparison with OpenAI modelsHands-on exploration of the UI and APIReal-world applications and use casesSection 3: Run DeepSeek R1 LocallyComplete Ollama setup guideQuick-start implementation (under 2 minutes)Performance optimization techniquesTroubleshooting common issuesSection 4: Build Agents with DeepSeek R1Introduction to AI agentsCrewAI framework integrationBuilding complex agent systemsReal-world agent applicationsOperator agent implementationSection 5: Run DeepSeek R1 on Android DevicesMobile AI fundamentalsStep-by-step Android setupOptimization for mobile devicesBuilding mobile AI applicationsSection 6: DeepSeek R1 RAG ChatbotRAG architecture deep diveDocument processing techniquesVector database integrationBuilding a production-ready chatbotPDF processing implementationSection 7: SummaryBest practices and guidelinesProduction deployment strategiesFuture developments and updatesRequirements:Basic Python programming knowledgeUnderstanding of basic ML conceptsComputer capable of running Python applicationsAndroid device (for mobile section)By the end of this course, you'll be able to:Build production-ready AI applications using DeepSeek R1Create sophisticated agent systems for task automationImplement RAG systems for custom knowledge basesDeploy AI applications on both desktop and mobile platformsOptimize performance for various use casesWhether you're looking to reduce dependency on cloud AI services or build cutting-edge applications with open-source technology, this course provides everything you need to master DeepSeek R1 and create powerful AI solutions.Join thousands of developers who are already leveraging DeepSeek R1 to build the next generation of AI applications. Start your journey into the future of AI development today!

Who this course is for:
Software developers wanting to build AI applications without cloud dependencies, Machine learning engineers looking to leverage open-source LLMs, Technical professionals seeking alternatives to OpenAI's models, System architects designing AI-powered applications, Technical Founders evaluating DeekSeek-R1 to work with their workflows