Free Download Generative AI Real world Projects Build End-to-End LLM AppsPublished 1/2026
Created by Shan Singh
MP4 |
Video: h264, 1920x1080 |
Audio: AAC, 44.1 KHz, 2 Ch
Level: All |
Genre: eLearning |
Language: English |
Duration: 29 Lectures ( 2h 33m ) |
Size: 2.74 GB
Build real-world Generative AI applications using LLMs (ChatGPT, Gemini, LLaMA) by completing hands-on GenAI projects used in industry.
Understand the complete GenAI pipeline - prompt design, embeddings, vector databases, RAG (Retrieval-Augmented Generation), inference, and evaluation.
Develop GenAI apps using Python - work with text, images, APIs, and tools like LangChain, FAISS, and OpenAI/Gemini SDKs.
build end-to-end pipelines including data ingestion, preprocessing, RAG, tool calling, and production-ready inference.
RequirementsBasics of Python programming is recommended !
DescriptionThis project-based Generative AI course is designed to help you build real-world LLM applications end to end, using industry-leading models like LLaMA, Google Gemini, and OpenAI GPT.Instead of focusing on theory alone, this course follows a hands-on, practical approach, where you will design, develop, and deploy 3 production-style GenAI projects that mirror real industry use cases.By the end of this course, you will have a strong Generative AI project portfolio, real experience working with LLM APIs, RAG systems, and vector databases, and a clear understanding of how modern AI-powered products are built and deployed in real-world environments.What You Will Build (Real-World Projects) :Project 1 : Cold Email Generator using LLaMA 3.3Build an AI-powered cold email generator that:Analyzes job
descriptions or business
requirementsExtracts relevant skills and contextAutomatically generates personalized, high-quality cold emailsThis project demonstrates how companies use open-source LLMs like LLaMA for sales automation and outreach. Project 2 : Text-to-SQL Generator using Google GeminiCreate an intelligent system that:Converts natural language questions into SQL queriesWorks on real database schemasEnables non-technical users to query databases using plain EnglishThis project reflects real enterprise use cases in data analytics, business intelligence (BI), and AI-driven decision-making systems.Project 3 : Food Calorie Detector using OpenAI GPTDevelop a multimodal AI pipeline that:Takes food images as inputExtracts food information using vision modelsRetrieves verified nutritional dataGenerates structured calorie, protein, fat, and carb insights using GPTThis project showcases end-to-end GenAI workflows, combining computer vision, retrieval-augmented generation (RAG), and LLM reasoning.What You Will Learn ?How Generative AI and LLMs work in real applicationsPrompt engineering techniques for reliable and accurate outputsBuilding RAG (Retrieval-Augmented Generation) systemsWorking with embeddings and vector databasesUsing LLaMA, Google Gemini, and OpenAI GPT APIsDesigning scalable and modular GenAI pipelinesHandling hallucinations, cost optimization, and production challengesBest practices for deploying GenAI solutions responsiblyWhy This Course Is Different ?100% project-based learning Real industry-style use cases (not toy examples) Multiple LLM providers: OpenAI, Google Gemini, LLaMA Focus on end-to-end GenAI system design Portfolio-ready projects for interviewsBy completing this course, you won't just understand Generative AI -you'll know how to build, apply, and explain GenAI solutions confidently in real-world scenarios. Enroll now and start building production-ready Generative AI applications.
Who this course is forBeginners stepping into Generative AI through real-world GenAI projects
Anyone learning GenAI with Python and wanting to build practical AI applications
Data Analysts / Developers who want to add GenAI & LLM skills to their profile
Software Engineers interested in building AI-powered products using LLMs
Professionals transitioning into GenAI roles like GenAI Engineer or AI Developer
Students and freshers who want hands-on experience with ChatGPT, Gemini, and LLaMA
AI enthusiasts curious about building RAG systems, and AI assistants
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