Generative AI Training Program - Live
-1669185704.svg)
Become an industry-ready Gen AI Developer from the ground up.
- Master the end-to-end lifecycle: design, build, and deploy advanced solutions.
- Advance from Python and Deep Learning to context-aware apps with LangChain and RAG.
- Customize local LLMs and build autonomous workflows with Crew AI and n8n.
- Evaluate and deploy your AI systems to the cloud using Docker and FastAPI.
Still Confused, Get free Counselling to upskill your carrer, Call us at +91- 9217990450
Course Overview
24/7 AI Doubt Support
Get instant help anytime, anywhere
IBM & GeeksforGeeks Certification
Earn industry-recognized certification
Modern AI Toolkit
Work with the latest AI tools & technologies
Skills You'll Gain
Key Gen AI concepts you'll build and master.
Deep Learning & Transformers
Prompt Engineering
RAG Systems
LLM Fine-tuning
AI Agents
Model Context Protocols (MCP)
Model Evaluation
MLOps & Deployment
Tech Stack You'll Learn
Tools and technologies you'll work with
Hugging Face
LangChain
Ollama
Crew AI
n8n
FastAPI
Gradio & Chainlit
Docker
DeepEval & LangSmith
Projects You'll Build
Real-world projects to apply your skills
Autonomous Market Research AgentBuild an enterprise-grade multi-agent AI system using CrewAI that autonomously gathers market intelligence, analyzes industry trends, synthesizes insights from multiple sources, and generates structured, decision-ready reports with minimal human intervention.
Text-to-SQL AppDevelop an intelligent Text-to-SQL application that converts natural language into optimized SQL queries using RAG, schema-aware validation, and LLMs, enabling users to interact with complex databases through conversational AI while ensuring accuracy and reliability.
Semantic Search EngineBuild a production-ready semantic search engine powered by Vector Databases, embeddings, and Retrieval-Augmented Generation (RAG) to retrieve context-aware results from unstructured data with high accuracy, scalability, and enterprise-grade performance.
Multimodal Text-Image AppCreate a multimodal AI application capable of understanding, analyzing, and reasoning across text and images using advanced vision-language models, enabling intelligent document processing, visual question answering, and real-world AI automation workflows.
QLoRA Fine-Tuned LLMFine-tune an open-source Large Language Model using PEFT and QLoRA techniques to build domain-specific AI assistants with improved response quality, reduced training costs, optimized inference performance, and production-ready deployment capabilities.
End-to-End Generatice AI CapstoneDesign, build, evaluate, and deploy a complete Generative AI solution by integrating LLMs, RAG pipelines, vector databases, Docker, cloud infrastructure, and automated evaluation frameworks to create a production-ready, enterprise-scale AI application.
Course Syllabus
Week 1: Python Basics for Data Science
Day 1: Python Basics for Data Science
- Introduction to Google Colab
- Data Types and Typecasting
- Conditionals, and Loops
Day 2: Python Basics for Data Science
- List Comprehensions
- Functions, and Lambda Functions
- Randomization
- Inputs in Python
Week 2: Feedforward Neural Networks
Day 1: Feedforward Neural Networks
- Understanding the AI Glossary
- Perceptron, and Activation Functions
- Layers and MLPs
- Understanding the Training Loop
Day 2: Backpropagation
- Backpropagation by Hand
- Loss and Error Metrics
- Project: MNIST Handwritten Digits Classification
Week 3: More Neural Networks
Day 1: Convolutional Neural Networks
- What is an image?
- Understanding kernels and convolutions
- Convolution Layers, Pooling Layers
- Convolutional Neural Networks
- Project: Cats vs Dogs Classification
Day 2: Understanding the Intuition of Recurrent Neural Networks
- What is a Sequence?
- What is a Seq2Seq Model?
- The RNN Cell
- The LSTM Cell
- Long-term Dependencies: Why RNNs are no longer relevant
Week 4: Transformers - Conceptuals
Day 1: Transformers - Conceptual Knowledge
- Seq2Seq Models with RNN
- What is Attention?
- A High-Level Look at Transformers
- Encoders
- Self-Attention
Day 2: Transformers - Conceptual Knowledge
- How are Attention Scores calculated?
- Multi-Headed Attention
- Positional Encoding
- Residuals & Layer Normalization
- Decoders
- Linear and Softmax Layer
- Revisiting the High-Level Look
