Hands-On Generative Ai Course From Beginner To Advanced
 

Learn, Build Application, Deploy and Apply Generative AI with With Langchain, Huggingface & Agentic AI
What you'll learn
Understand the foundations of Generative AI and deep learning, including neural networks, probability, statistics, linear algebra, GANs, VAEs, transformers
Build and implement real-world Generative AI applications using TensorFlow, Hugging Face, LangChain, LangGraph, and modern AI development frameworks
Develop, fine-tune, and deploy Large Language Model (LLM) solutions, including AI agents, Retrieval-Augmented Generation (RAG) systems, and chatbot applications
Create end-to-end AI-powered solutions by integrating vector databases, Streamlit frontends, multimodal models, and advanced Generative AI techniques
Requirements
You must be curious and passion for learning Generative AI
Description
Unlock the power of Generative AI through a comprehensive, project-driven learning experience designed to take you from foundational concepts to advanced AI application development. This course provides a deep understanding of the technologies driving modern artificial intelligence, including neural networks, transformers, large language models (LLMs), retrieval-augmented generation (RAG), AI agents, vector databases, and multimodal AI systems.
Beginning with the mathematical foundations of AI, learners will explore probability, statistics, linear algebra, neural networks, optimization techniques, and deep learning architectures. The course then progresses into advanced generative models such as GANs and Variational Autoencoders, enabling students to understand how AI systems generate realistic content and data.
Participants will gain hands-on experience with transformer architectures, attention mechanisms, GPT, BERT, and other large language models while implementing real-world projects. The curriculum covers fine-tuning techniques using LoRA and QLoRA, prompt engineering, Hugging Face tools, LangChain, LangGraph, AI agents, and modern AI development workflows.
A major focus of the course is practical application. Students will build end-to-end AI solutions, including intelligent chatbots, RAG-powered applications, image generation systems, image and video captioning tools, and AI-powered web applications using Streamlit, ChromaDB, and advanced LLM frameworks. Through multiple mini-projects and capstone projects, learners will develop a strong portfolio showcasing industry-relevant skills.
The course also explores emerging AI technologies such as Model Context Protocol (MCP), Ollama, Unsloth fine-tuning, Mixture of Experts, DeepSeek architecture, diffusion models, vision transformers, CLIP, multimodal AI systems, and model distillation techniques. By the end of the program, learners will possess the knowledge and practical expertise required to design, build, deploy, and scale modern Generative AI applications in real-world environments.
Who this course is for
It is for those who wish to master Generative AI