Udemy - Build AI Apps with Qwen 2.5, Deepseek & Ollama

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Free Download Udemy - Build AI Apps with Qwen 2.5, Deepseek & Ollama
Published: 3/2025
Created by: Amrit Ramchandani
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 14 Lectures ( 1h 9m ) | Size: 1.2 GB

Build real-world AI-powered applications on your local computer using Qwen 2.5, DeepSeek, and Ollama.
What you'll learn
Understand what are large language models (LLMS) and how it works
Build AI-powered applications using Deepseek, Qwen2.5 and Ollama
Setting Up and Running Qwen 2.5 and DeepSeek Locally Using Ollama
Create UI Application that Interacts with Large Language Model such as Qwen and Deepseek
Use Ollama CLI with Qwen2.5 and Deepseek
Basic command-line proficiency (executing scripts, installing packages)
Requirements
A computer with macOS, Windows, or Linux
Internet connection
Optional: Python Proficiency for Enhancing Real-World Cases Presented in the Course with Greater Complexity
Essential command-line skills (running scripts, managing packages)
Description
Break Free from the Cloud-Build AI on Your TermsFor years, cloud-based AI has been the go-to solution for developers. The convenience of API-driven models made it easy to integrate AI into applications without worrying about infrastructure. However, this convenience comes with trade-offs-high costs, data privacy concerns, and reliance on third-party providers.As AI adoption grows, more developers are rethinking their approach and turning to self-hosted AI models that run entirely on their local machines. This shift isn't just about reducing cloud expenses-it's about full control, performance, and independence.Why Developers Are Moving to Local AIPerformance Without LatencyCloud AI introduces delays. Each request must travel across the internet, interact with remote servers, and return results. Running AI locally eliminates network lag, making AI-driven applications significantly faster and more responsive. Privacy and Data SecurityMany industries-especially healthcare, finance, and legal sectors-require strict data security. Sending sensitive information to cloud providers raises privacy risks. By running AI models locally, developers keep their data in-house, ensuring compliance with security regulations.Cost EfficiencyCloud-based AI pricing often scales unpredictably. API calls, storage, and processing costs can quickly add up, making long-term AI development expensive. Local AI eliminates recurring fees, allowing developers to work with AI at no extra cost beyond initial hardware investment.Customization and OptimizationCloud AI models come as pre-trained black boxes with limited flexibility. Developers who want fine-tuned AI for specific use cases often hit restrictions. Self-hosted models allow for deeper customization, training, and optimization.Key Tools Powering Local AI DevelopmentTo build AI applications without cloud dependencies, developers are turning to three powerful tools:Qwen 2.5 - A robust language model designed for text generation, automation, and reasoning. Unlike cloud-based AI, it runs entirely on local hardware, giving developers full control over processing and execution.Deepseek - An efficient AI model that applies distillation techniques to reduce computational costs while maintaining high performance. This makes it ideal for developers who need lightweight, high-speed AI without requiring powerful GPUs.Ollama - A streamlined model management tool that simplifies loading, running, and fine-tuning AI models locally, ensuring smooth deployment and integration into projects.Building AI on Your Own TermsWhether you're working on intelligent automation, AI-driven assistants, or advanced text generation, local AI offers unparalleled control and flexibility.Developers who make the shift gain: Full AI Independence - No reliance on cloud APIs or external services. Privacy & Control - All processing happens on local machines, ensuring data security. Hands-on AI Development - Direct interaction with models instead of relying on third-party platforms. Optimization Capabilities - The ability to fine-tune AI models for performance and efficiency. Scalability Without Costs - AI usage no longer depends on pay-per-use pricing models.As the AI landscape evolves, local AI isn't just an alternative-it's the future. By understanding how to deploy, optimize, and build with self-hosted models, developers can break free from cloud restrictions and unlock AI's full potential.Ready to Take AI Into Your Own Hands? Let's Begin!
Who this course is for
Software engineers looking to develop applications using local LLMs like Qwen and DeepSeek
Full-stack developers looking to integrate LLM models into web applications
Students and researchers exploring the execution of local AI models
Python programmers seeking to integrate AI into their projects
AI/ML beginners keen to gain practical experience in AI development
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