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Complete Generative AI Mastery Course: LLM, RAG & Vision App
Rating: 4.9 out of 5(39 ratings)
1,308 students

Complete Generative AI Mastery Course: LLM, RAG & Vision App

Build real-world Generative AI apps using Python, LangChain, LLMs, RAG & Vision AI with 12+ hands-on projects mastery
Created byMuhammad Yaqoob G
Last updated 12/2025
English
English [Auto],

What you'll learn

  • Understand the core concepts of Generative AI, including Transformers, GANs, Autoencoders, and RAG frameworks
  • Build end-to-end LLM applications using LangChain, Llama 3, BGE, and Faiss for text and document intelligence
  • Implement Retrieval-Augmented Generation (RAG) systems with vector databases like Milvus for contextual AI chatbots
  • Develop custom GAN models and use Stable Diffusion for realistic image generation and creative AI workflows
  • Master text-image similarity search with CLIP embeddings and large-scale vector indexing
  • Create multimodal AI systems combining text, image, and video understanding using VILA 2.0 and T5
  • Learn how to train, fine-tune, and deploy Generative AI models for real-world use cases
  • Understand transformer internals, including multi-head attention, positional embeddings, and feedforward blocks
  • Explore Autoencoders and Variational Autoencoders (VAE) for denoising, reconstruction, and latent-space learning
  • Build AI-powered applications like chatbots, video search systems, and intelligent assistants from scratch
  • Learn to integrate Faiss and Milvus for high-performance vector search and retrieval in production
  • Develop production-ready AI pipelines, covering embedding generation, inference optimization, and deployment
  • Gain hands-on experience through 12+ advanced projects blending theory with practical coding
  • Acquire job-ready AI engineering skills to design, build, and deploy scalable Generative AI solutions

Course content

22 sections • 46 lectures • 8h 17m total length

  • Introduction3:26

    Learn practical generative AI with real-world projects, building and deploying production-grade LLM apps, image generation with GAN and stable diffusion, and text-to-image and video search.

Requirements

  • Basic Python programming knowledge is required. Know variables, data types, control flow, functions, and basic scripting to follow along with labs.
  • Windows PC or Laptop with 4GB+ RAM recommended. Ensure Windows OS is installed, there is sufficient disk space for datasets, and essential developer tools ready
  • A GPU is optional but highly recommended for faster training and real-time experiments; without one, expect much longer training duration and slower performance.
  • The projects and exercises were developed and tested on Windows systems; guides, scripts, and troubleshooting steps are tailored exclusively for Windows learners.

Description

Step into the world of Generative AI and Large Language Models (LLMs) with this Complete Generative AI Mastery Course, an immersive, project-driven program designed to take you from fundamentals to professional-level mastery. In this Generative AI course, you will build production-grade AI applications using industry-standard frameworks such as LangChain, LLaMA 3, FAISS, and Milvus — the same technologies powering real-world enterprise and research-grade AI systems.

The course begins with a deep dive into the core concepts of Transformers, GANs, embeddings, and foundation models, helping you understand how modern generative models process and generate human-like content. You will then explore Retrieval-Augmented Generation (RAG), vector databases, and multimodal AI to create powerful, context-aware, and intelligent solutions for text, image, and video understanding.

Through 12+ guided, hands-on projects, you will build:

  • AI chatbots powered by LLMs

  • Intelligent document retrieval & RAG systems

  • Image generation & Vision AI applications

  • Semantic similarity search engines

  • AI-powered video retrieval systems

You will work with cutting-edge models and architectures, including T5 and multimodal models, while applying best practices for real-world system design.

By the end of this course, you will master the complete Generative AI pipeline — from data ingestion, embeddings, model chaining, fine-tuning, and optimization to scalable deployment across edge, cloud, and hybrid environments.

Whether you are a Python developer, AI enthusiast, data scientist, researcher, or tech innovator, this course equips you with the practical skills and deep technical understanding needed to design, build, and deploy next-generation LLM and Vision AI systems from scratch.

Who this course is for:

  • This course is designed for learners who want to go beyond theory and gain hands-on mastery of Generative AI tools, models, and real-world applications.
  • AI & ML Beginners who have basic Python knowledge and want to quickly step into the world of Generative AI with structured, practical guidance.
  • Data Scientists & ML Engineers looking to strengthen their skills in autoencoders, GANs, transformers, retrieval-augmented generation (RAG), and diffusion models.
  • Software Developers interested in integrating generative AI into apps with FastAPI, Tkinter, LangChain, Milvus/FAISS, and Docker deployments.
  • Researchers & Students who want to deeply understand how models like GPT, BERT, LLaMA, and Stable Diffusion work internally and apply them in projects.
  • Tech Enthusiasts eager to explore generative applications such as image denoising, text-to-image generation, AI-powered search, face recognition, video similarity, and multimodal tasks.
  • Entrepreneurs & Innovators planning to build AI-driven products in fields like chatbots, recommendation systems, creative design, surveillance, and smart automation.
  • No prior deep learning expertise is strictly required, but familiarity with Python and basic ML concepts (supervised vs. unsupervised learning) will help you get the most out of this course.

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