Generative AI Training
Master the Future with Our Generative AI Training Course! Explore cutting-edge tools like ChatGPT and DALL·E while learning essential concepts, real-world applications, and hands-on projects. Build skills in AI content creation, prompt engineering, and model integration, and discover how Generative AI is transforming industries like marketing, healthcare, and software development.
8 | 9 monthsCourse duration
Classroom | OnlineMode of Delivery
09Capstone projects
Why should you do this course?
Learn and grow as a developer with our project based courses.
Turn Prompts into Possibilities
Learn how to build powerful web apps from scratch using MongoDB, Express, React, and Node. This course is highly practical and project-based.
Design Smarter, Build Faster, Think AI
Master both frontend and backend technologies and become a job-ready full stack developer, capable of handling entire application flows.
Unleash the Power of AI Creativity
MERN stack is one of the most popular stacks used by top companies. This course prepares you for full stack developer roles with strong job prospects.
Learn. Innovate. Lead with Generative AI
Gain hands-on experience with Git, GitHub, REST APIs, JWT, MVC architecture, and deployment strategies used in real-world teams.
Starting from₹ 60000₹ 40000
LIVE BATCHKey Highlights
Generative AI courselearn Generative AI onlineGenerative AI training with projects
Live Projects
8 | 9 months duration
Certificate of Excellence/Completion
Placement assistance
Syllabus
Quickstart
Overview of the Journey
We offer live classes with expert instructors, weekly assignments to reinforce your learning, and fully practical training focused on real-world skills. You’ll work on hands-on projects throughout the course to build experience and confidence.
Python Programming Language
Python Control Flow
Understand how to control the execution of your Python programs using conditional statements, loops, and exception handling — essential for creating dynamic and responsive AI applications. Mastering control flow is key to building intelligent logic in Generative AI solutions.
Data Structure using Python
Explore essential Python data structures like lists, tuples, dictionaries, and sets to efficiently organize and manage data in AI workflows. Strong data handling skills are crucial for processing and manipulating complex datasets in Generative AI projects.
Functions in Python
Learn to create reusable, modular code with Python functions that simplify complex tasks and improve code clarity. Functions are fundamental for building scalable Generative AI applications and organizing AI model workflows efficiently.
Importing Creating Modules and Packages
Master the art of organizing your Python code by creating custom modules and packages, and learn to efficiently import external libraries. This skill ensures cleaner, maintainable, and scalable AI project structures in Generative AI development.
File Handling
Learn to read, write, and manage data files seamlessly using Python’s file handling techniques. This is a crucial skill for storing, retrieving, and preprocessing datasets — the foundation of every successful Generative AI model.
Exception Handling
Develop robust AI applications by mastering exception handling techniques in Python. Learn to detect, manage, and resolve runtime errors gracefully, ensuring smooth execution and stability in complex Generative AI workflows.
Object Oriented Programming
Build powerful, scalable, and reusable AI solutions using Object-Oriented Programming (OOP) concepts like classes, objects, inheritance, and polymorphism. OOP is essential for structuring advanced Generative AI applications with clean and maintainable code.
Streamlit with Python
Streamlit Framework
Learn to build interactive and visually appealing AI web applications effortlessly using Streamlit. This framework allows you to deploy Generative AI models with user-friendly interfaces, making AI insights accessible and actionable in real time.
Machine Learning for NLP
Machine Learning Classification
Understand the core machine learning techniques that power Natural Language Processing (NLP) applications. Learn to build models for text classification, sentiment analysis, and language understanding — the foundational skills behind modern Generative AI systems.
Deep Learning for NLP
Deep Learning using keras and tensorflow
Master advanced deep learning techniques tailored for Natural Language Processing tasks. Explore neural networks, RNNs, LSTMs, and Transformer-based architectures that drive cutting-edge Generative AI applications like chatbots, text generators, and language models.
Transformers
Building block of generative AI
Dive into Transformer architectures — the backbone of modern Generative AI models. Learn how attention mechanisms and encoder-decoder structures revolutionize tasks like text generation, translation, and summarization with unmatched accuracy and efficiency.
Introduction to Generative AI
Generative AI
Get a comprehensive overview of Generative AI, its concepts, and real-world applications. Understand how AI models create new content — text, images, audio, and more — and discover the technologies driving innovations in creative automation and intelligent systems.
Getting started with openAI and Langchain
OpenAI
Explore OpenAI’s cutting-edge AI technologies and platforms that power state-of-the-art language models like GPT. Understand how to leverage OpenAI’s tools to create advanced Generative AI solutions for diverse industries and applications.
Langchain
Discover LangChain, a powerful framework designed to simplify the development of AI applications by managing complex workflows, chaining language model calls, and integrating with external data sources. Perfect for building scalable and dynamic Generative AI solutions.
Building basic LLM Applications
Large Language Models
Learn how to develop foundational applications using Large Language Models (LLMs). This includes creating text generation, summarization, and conversational AI systems that harness the power of advanced natural language understanding.
Amazon Web Services
AWS EC2 and IAM
Learn to deploy and manage virtual servers using AWS EC2 for hosting AI models, while mastering IAM to securely control user access and permissions. These services ensure scalable, secure infrastructure for reliable Generative AI applications.
AWS Bedrock and Sagemaker
Discover AWS Bedrock for building and scaling generative AI applications with pre-trained foundation models, and master SageMaker to develop, train, and deploy custom machine learning models efficiently. Together, they empower end-to-end AI innovation on the cloud.
AWS Lambda and API Gateway
Learn to create scalable, serverless AI applications by running code with AWS Lambda and managing APIs securely with API Gateway. This combination enables efficient deployment and integration of Generative AI services without managing servers.
Retrieval-Augmented Generation (RAG)
RAG Model
Explore the RAG technique that combines powerful retrieval methods with generative models to enhance AI responses using relevant external data. This approach improves accuracy and context-awareness in advanced Generative AI applications.
Building Chatbot with Llama2, Langchain & Streamlit
Basic Chatbot using Llama2 and Streamlit
Build your first chatbot by combining Llama2’s advanced language capabilities with Streamlit’s simple web app interface. This hands-on module helps you create an interactive AI assistant with minimal coding effort.
Basic Chatbot using Langchain and Streamlit
Create a foundational chatbot by leveraging LangChain’s powerful orchestration of language model workflows alongside Streamlit’s intuitive interface, enabling you to develop and deploy conversational AI applications quickly and effectively.
Fine Tuning of Foundation Model on Custom data
Model Optimization
Master techniques to improve the efficiency, speed, and accuracy of AI models, ensuring optimal performance for Generative AI applications while reducing resource consumption and deployment costs.
Prompt Engineering
Learn Prompts
Gain practical skills in designing and refining prompts to interact effectively with AI models, enabling you to extract the best responses and tailor outputs for diverse Generative AI use cases.
Interview Question Creator
Introduction and architecture explanation of project
Gain a clear understanding of your Generative AI project’s goals, design principles, and system architecture. This foundation ensures smooth development, integration, and deployment of scalable and efficient AI solutions.
local project setup using conda and vscode
Learn how to create a robust local development environment with Conda for package management and VSCode for efficient coding. This setup streamlines your Generative AI project workflow, ensuring smooth development and testing.
Lanchain for data loading and data transformation
Master using LangChain to efficiently load, preprocess, and transform data, enabling seamless integration of diverse data sources into your Generative AI workflows for enhanced model performance and accuracy.
Integrate OpenAI API
Learn to connect and utilize OpenAI’s powerful language models via API integration, enabling your applications to generate, analyze, and interact with natural language data effectively within your Generative AI projects.
OpenAI's API for automated qa generation
Discover how to leverage OpenAI’s API to automatically create high-quality question-and-answer pairs, streamlining content creation and enhancing AI-driven knowledge bases and learning tools.
Vector Database for embedding store
Understand how to use vector databases to efficiently store and search high-dimensional embeddings, enabling fast and accurate retrieval of relevant information in Generative AI applications like semantic search and recommendation systems.
Using Streamlit for Web Apps
Learn to quickly build and deploy interactive, user-friendly web applications with Streamlit, making it easy to showcase and share your Generative AI models and insights with real-time user engagement.
Version Control with Git
Master Git to track, manage, and collaborate on your Generative AI project code effectively, ensuring smooth teamwork, version history, and reliable project development workflows.
Deploy to AWS EC2
Learn to launch and manage your Generative AI applications on AWS EC2 virtual servers, ensuring scalable, secure, and reliable cloud deployment for real-world use cases.
Finance Chatbot
Project overview, tech stack exploration
Gain a comprehensive understanding of the project goals and explore the essential technologies, tools, and frameworks used to build effective Generative AI solutions, ensuring a strong foundation for successful implementation.
local project setup using conda and vscode
Learn to set up a streamlined local development environment with Conda for efficient package management and VSCode for powerful, user-friendly coding, enabling smooth development of Generative AI projects.
Langchain for secure data storage/retrieval
Discover how to use LangChain to securely manage and access data within your AI workflows, ensuring safe, efficient, and compliant handling of sensitive information in Generative AI applications.
Google Gemini API Integration
Learn to connect and leverage Google Gemini’s powerful AI capabilities through API integration, enabling your Generative AI applications to utilize advanced multimodal models for enhanced performance and versatility.
Accessing Google Gemini Pro
Get hands-on guidance to access and utilize Google Gemini Pro’s advanced AI features, empowering your Generative AI projects with cutting-edge multimodal models for superior intelligence and creativity.
Flask Web Framework
Learn to build lightweight, flexible web applications using Flask, a Python-based framework perfect for deploying Generative AI models with customized APIs and user interfaces.
AWS Services Overview
Get an insightful introduction to the wide range of AWS cloud services essential for building, deploying, and scaling Generative AI applications — from computing and storage to AI tools and security management.
Version Control with Git
Master the essentials of Git for tracking, managing, and collaborating on your AI projects. Ensure organized development, maintain version history, and enable smooth teamwork throughout your Generative AI workflows.
Deploy to AWS EC2
Learn to host and run your Generative AI applications on AWS EC2 virtual servers, ensuring scalable, secure, and high-performance cloud deployment for production-ready solutions.
Testing and debugging
Develop essential skills to systematically test and debug your Generative AI applications, ensuring accuracy, stability, and optimal performance before deployment and during real-time operations.
Retrieval System using Generative AI
Project overview, tech stack exploration
Understand the project’s goals, workflow, and architecture while exploring the complete tech stack — from AI models and frameworks to APIs and cloud services — powering your Generative AI solutions.
local project setup using conda and vscode
Set up a clean, efficient local environment for your Generative AI projects with Conda for environment and package management, paired with VSCode for a seamless coding and debugging experience.
Llama Index Integration for text retrieval and search functionalities.
Learn to integrate LlamaIndex into your Generative AI applications to enable fast, accurate, and context-aware text retrieval and search functionalities, enhancing the intelligence and responsiveness of your AI solutions.
Flask Web Framework
Master the Flask web framework to build lightweight, flexible web applications and APIs, ideal for deploying and managing your Generative AI models with clean, scalable, and interactive user interfaces.
Google Gemini Pro Integration
Learn to integrate Google Gemini Pro’s powerful multimodal AI capabilities into your projects, enabling advanced text, image, and conversational intelligence within your Generative AI applications for next-gen experiences.
Vertex AI Introduction
Explore Google Cloud’s Vertex AI platform, designed to simplify building, deploying, and scaling machine learning models, empowering you to accelerate your Generative AI projects with managed services and robust tools.
Deployment to Google Cloud
Learn how to deploy your Generative AI applications on Google Cloud, leveraging its scalable infrastructure, managed services, and advanced AI tools for reliable, secure, and efficient cloud hosting.
FAISS for Efficient Search
Discover how to use FAISS, Facebook’s powerful similarity search library, to perform fast and scalable vector searches—crucial for building efficient retrieval and recommendation systems in Generative AI applications.
Design and conduct thorough testing to ensure system functionality
Learn to create comprehensive test plans and execute rigorous testing to validate your Generative AI system’s functionality, ensuring reliability, accuracy, and seamless user experience before deployment.
HR Q&A With Advanced RAG
Overview of Vectors, Embedding, Vector DB and Search
Understand the fundamentals of vector representations and embeddings that capture data meaning, and explore how vector databases enable fast, scalable similarity search—key components powering modern Generative AI applications.
Architecture for the Use Case
Explore the detailed system architecture tailored to your Generative AI use case, highlighting component interactions, data flow, and technology choices that ensure efficient, scalable, and robust AI solutions.
Environment Setup before coding
Learn how to prepare your development environment with essential tools, libraries, and configurations to ensure a smooth and efficient start for your Generative AI coding projects.
Data Load & Transformation
Master techniques to efficiently load and transform raw data into clean, structured formats, enabling accurate and meaningful analysis for your Generative AI models.
Embedding, Vector Store & Index
Understand how to convert data into embeddings, store them efficiently in vector databases, and create indexes for rapid similarity search—critical steps for building intelligent Generative AI applications.
LLM Creation + Context
Learn how to build and fine-tune large language models (LLMs) while effectively managing contextual information to enhance the relevance and accuracy of AI-generated outputs.
Frontend and Final Demo
Discover how to build an engaging user interface for your Generative AI application and showcase the complete project through a polished, interactive final demonstration.
End to End Demo
Experience a complete walkthrough of the project, demonstrating all stages from data preparation to model deployment, highlighting seamless integration and real-world functionality of your Generative AI solution.
Zomato Chatbot
Overview of the project
Get a comprehensive introduction to the project’s objectives, scope, and key components, providing a clear roadmap for developing a robust and impactful Generative AI solution.
project architecture explanation
Dive into the detailed architecture of the project, explaining each component’s role, data flow, and how they integrate to build a scalable and efficient Generative AI system.
chain lit complete walk through and uses
Explore ChainLit’s end-to-end framework for building conversational AI applications, including setup, workflow, and practical use cases to create interactive, scalable chatbots and AI assistants with ease.
loading opensource models using hugging face API
Learn how to easily access and deploy powerful open-source AI models from Hugging Face’s vast model hub via their API, enabling quick integration and experimentation in your Generative AI projects.
prompts creation for the chatbot
Master the art of crafting effective prompts that guide your chatbot’s responses, enhancing interaction quality, context understanding, and user satisfaction in Generative AI applications.
deploy to AWS using docker and ECR
Learn to containerize your Generative AI application with Docker and deploy it seamlessly to AWS Elastic Container Registry (ECR), enabling scalable, efficient, and secure cloud hosting.
deploy to telegram
Discover how to integrate and deploy your Generative AI chatbot on Telegram, enabling real-time user interaction through a popular messaging platform with easy-to-use bot APIs.
Why choose Datadrix?
Learn and grow as a developer with our project based courses.
Superb mentors
Best in class mentors from top Tech schools and Industry favorite Techies are here to teach you.
Industry-vetted curriculum
Best in class content, aligned to the Tech industry is delivered to you to ensure you Tech industry.
Project based learning
Hands on learning pedagogy with live projects to cover practical knowledge over theoretical one.
Superb placements
Result oriented courses across all genres, students as well as Working professionals.
Project based learning
Hands on learning pedagogy with live projects to cover practical knowledge over theoretical one.
Superb placements
Result oriented courses across all genres, students as well as Working professionals.
Certificate of completion
Joining DATADRIX means you'll create an amazing network, make new connections, and leverage diverse opportunities.
“Validate Your Expertise and Propel Your Career”
Expand Opportunities: Certifications to unlock new career opportunities, gain credibility with employers, and open doors to higher-level positions.
Continuous Growth: Certifications not only validate your current skills but also encourage continuous learning and professional development, allowing you to stay updated with the latest industry trends and advancements.
Certification: A testament to your skills and knowledge, certifications demonstrate your proficiency in specific areas of expertise, giving you a competitive edge in the job market.
Our Alumni's Are Placed At
See what students have to say
Joining DATADRIX means you'll create an amazing network, make new connections, and leverage diverse opportunities.
I joined Datadrix to learn Python and Data Engineering. Thanks to Om Arora for simplifying coding concepts and providing practical projects to work on.
Datadrix Institute helped me build a solid base in Python and Data Science. Special thanks to Nitin Shrivastav for his clear and practical teaching.
Thanks to Datadrix’s Data Analytics program, I cracked my interview confidently. Nitin Shrivastav’s sessions were insightful and very practical.
Loved learning Python and Data Science here. Datadrix has the best trainers and projects. Special thanks to Om Arora for his real-world examples.
Finally cracked my second job in data science after Datadrix’s training. Nitin Shrivastav’s SQL and Power BI sessions boosted my confidence.
Datadrix Institute made learning Web Development super fun! Om Arora’s support and practical project work made the course so much more valuable.
The Data Analytics course by Datadrix Institute was worth it. Nitin Shrivastav’s explanations on tools like Excel and Power BI made it easy.
Datadrix's Data Science program gave me clarity on statistics and ML. Om Arora explained tough topics in a very simple and relatable way.
Big thanks to Datadrix for helping me master Python programming. Nitin Shrivastav’s approach to teaching made coding fun and easy to follow.
Datadrix's Data Science program gave me clarity on statistics and ML. Nitin sir explained tough topics in a very simple and relatable way.
The Data Analytics course at Datadrix helped me land my job as a data analyst. Nitin Shrivastav’s clear and patient teaching style stood out.
The Python programming training was perfect for beginners. Thanks to Nitin Shrivastav for always clearing doubts patiently and giving real projects.
Frequently Asked Questions
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