19 Generative Ai Real Time Projects End To End

Master Generative AI with 20+ Real-World Projects: Build, Deploy & Scale End-to-End Solutions

What you’ll learn
Develop practical skills in building and deploying generative AI applications using LLM models.
Gain hands-on experience in training and fine-tuning LLM models using various datasets.
Create diverse applications leveraging the power of these LLM models.
End-to-end Projects Using LLM, VectorDB, Langchain, LlamaIndex, Flask, Streamlit, Chainlit and so on.
Gain hands-on experience in LLMOps
Master Google Vertex AI & AWS Bedrock for Generative AI Project Implementation

Requirements
Proficiency in Python Programming: Strong understanding of Python syntax and experience with data manipulation libraries (NumPy, Pandas).
Basic Deep Learning Knowledge: Familiarity with neural networks, backpropagation, and concepts like CNNs and RNNs.
Experience with ML Frameworks: Hands-on experience using TensorFlow, PyTorch, or Keras for model development and training.
Mathematics Foundation: Knowledge of linear algebra, calculus, and probability for comprehending model architecture.

Description
Welcome to the ultimate hands-on course on Generative AI, where theory meets practice through a series of 20+ real-world projects! This course is meticulously crafted to help you master cutting-edge Generative AI models like GPT, GANs, Transformers, Variational Autoencoders, and more by building complete, end-to-end solutions from scratch.This comprehensive course is designed to accommodate learners at different levels, making it ideal for both beginners and experienced AI professionals. Whether you are new to the field or have some foundational knowledge, you’ll start with a solid introduction to the core concepts of Generative AI. From there, we’ll dive into advanced topics, ensuring that by the end, you’ll have a deep understanding of the intricacies of various models, their applications, and implementation techniques.Throughout the course, you’ll work on projects spanning a wide range of domains, including natural language processing, image synthesis, video generation, code completion, music composition, and creative design. Each project is carefully structured to take you through every stage of the development process—from data collection and preprocessing to model building, optimization, and deployment strategies.By following our step-by-step tutorials, you will gain hands-on experience in building real-world AI applications, creating a rich portfolio of projects that showcase your skills. You’ll also learn about best practices for model performance optimization, cloud deployment, and scaling solutions for various business needs. This course includes source codes, project files, quizzes, and interactive exercises to help reinforce your learning.By the end, you’ll have the confidence and expertise to build, deploy, and scale state-of-the-art Generative AI solutions in diverse industries.

Overview
Section 1: Projects

Lecture 1 Text summarization with hugging face

Lecture 2 Text to Image generation with LLM with hugging face

Lecture 3 Text to speech generation with LLM with hugging face

Lecture 4 Telegram bot using OpenAI

Lecture 5 Finetuning of GPT-3 model for text classification

Lecture 6 Audio Transcript Translation with Whishper

Lecture 7 Image generation with DALL-E

Lecture 8 Custom Website Chatbot

Lecture 9 Custom Website Chatbot using Open source LLMs

Lecture 10 Build a Q&A App with RAG using Gemini Pro and Langchain

Lecture 11 Financial Stock Analysis using LlamaIndex

Lecture 12 End to End Medical Chatbot Project with LLM, Pinecone, LangChain

Lecture 13 End to End Source Code Analysis with LangChain, OpenAI and ChromaDB

Lecture 14 Implementing Zomato chatbot with Chainlit

Lecture 15 How to Deploy Generative AI Application as CICD on AWS

Lecture 16 RAG on Vertex AI with Vector Search and Gemini Pro

Lecture 17 LLM powered application on Vertex AI

Lecture 18 Hands-on AWS Bedrock

AI Enthusiasts & Beginners: Individuals looking to explore and build a solid foundation in Generative AI through practical projects.,Data Scientists & Machine Learning Engineers: Professionals wanting to expand their skillset by implementing advanced AI models.,Software Developers: Developers interested in transitioning to AI roles and building real-world AI applications.,Researchers & Academics: Those looking to apply Generative AI techniques in research or academic projects.