Master Generative AI & Agents Course
Master Generative AI concepts, machine learning foundations, deep learning, large language models, AI automation, RAG, AI agents and modern AI application development through a structured, job-oriented training program.
About the Generative AI & Agents Program
This program provides a structured introduction to Artificial Intelligence, Generative AI, Machine Learning and Deep Learning, progressing into Large Language Models, AI automation, Retrieval-Augmented Generation, AI agents and production-oriented AI applications.
The curriculum covers both foundational concepts and practical modern AI technologies, including prompt engineering, n8n, LangChain, LlamaIndex, FastAPI, vector databases, multimodal AI and chatbot development.
Key Highlights
Generative AI Foundations
Understand AI, machine learning, deep learning, Generative AI, LLMs and multimodal AI systems.
Machine Learning & Data Science
Learn datasets, data preparation, supervised and unsupervised learning, regression, trees and model evaluation.
Deep Learning & Transformers
Explore neural networks, CNNs, LSTM concepts, embeddings, attention and transformer architecture.
AI Automation & Agents
Work with workflows, APIs, n8n, RAG, chatbots with memory, tool calling and AI agent workflows.
Practical AI Applications
Build practical applications including an OpenAI integration chatbot project and AI content automation workflows.
Modern AI Development
Use FastAPI and modern AI frameworks and tools for application development and production workflows.
Course Overview
A focused 3-month program designed around 60 hours of training, covering the complete learning journey from AI fundamentals to Generative AI, machine learning, deep learning, AI automation and agent-based applications.
Program Curriculum
The curriculum follows the brochure and is organised into clear learning modules so the detailed syllabus remains easy to explore without making the page unnecessarily long.
Module 1Introduction to AI & Generative AI⌄
- Introduction to AI
- Why AI Became Important
- Evolution of AI
- AI Ecosystem Overview
- How AI Systems Work
- Machine Learning and Deep Learning Overview
- Generative AI Introduction
- Large Language Models (LLMs)
- Multimodal AI Systems
- Modern AI Tools Practical
- AI Ethics & Responsible AI
- OpenAI Integration Chatbot Project
Module 2Machine Learning & Data Science Foundations⌄
- Introduction to Machine Learning
- Data for AI Systems
- Dataset Understanding
- Data Preparation Basics
- AI Development Workflow
- Basic Math for AI (Simplified)
- Supervised Learning
- Linear Regression
- Model Error & Improvement
- Logistic Regression
- Decision Trees
- Unsupervised Learning
- Principal Component Analysis (PCA)
- Overfitting & Underfitting
- Model Evaluation
Module 3Deep Learning & Neural Networks⌄
- Neural Network Practical Concepts
- Activation Functions (Simplified)
- Neural Prediction Workflow
- Backpropagation Concept
- CNN (Convolutional Neural Networks) for Image AI
- Image Processing Workflow
- Sequential AI Models
- LSTM & Memory Concept
- Word Embeddings & Semantic Meaning
- Attention Mechanism Deep Dive
- Transformer Architecture (Part 1)
- Transformer Architecture (Part 2)
- Positional Encoding Basics
- Model Design Thinking
Module 4AI Automation & Agents⌄
- Introduction to Automation
- Workflow Concept
- n8n Fundamentals
- API Basics
- Prompt Engineering Masterclass
- Connecting AI Models
- AI Content Automation
- Introduction to RAG (Retrieval-Augmented Generation)
- Vector Databases & Semantic Search
- Introduction to LangChain & LlamaIndex
- Chatbots with Memory
- AI Agents Fundamentals
- Tool Calling & Agent Workflows
- FastAPI for AI Applications
- Deployment & Production Workflows
Tools & Technologies You Will Work With
The brochure highlights a modern AI development toolkit covering model frameworks, orchestration, automation, application development, vector databases and AI assistants.
Practical AI Applications
AI Chatbot Development
Explore OpenAI integration and chatbot development, including chatbot memory concepts.
AI Automation
Understand workflows, APIs, n8n and AI content automation for connected AI processes.
RAG & Semantic Search
Learn retrieval-augmented generation, vector databases and semantic search concepts.
AI Agent Workflows
Study AI agents, tool calling, agent workflows and connecting AI models.
AI Application Development
Use FastAPI and production workflow concepts for modern AI applications.
Responsible AI
Understand AI ethics and responsible AI as part of modern AI development.
Learning & Training Support
Pre-Training Assessment
Assess current skill level and learning needs before training begins.
Expert-Led Classes
Learn through instructor-led training supported by industry-focused teaching.
Post-Training Evaluation
Track learning progress and understand how well the skills have been mastered.
Why Choose NIPSTec
NIPSTec's brochure highlights more than 25 years of training experience, industry-relevant curriculum, qualified and experienced trainers, job-oriented programs and a PAN India reach.
25+ Years of Experience
Training experience across students and corporates.
Industry-Relevant Curriculum
A structured learning path covering modern AI technologies and practical tools.
Qualified & Experienced Trainers
Instructor-led learning designed to support practical understanding.
Job-Oriented Programs
Training structured around practical skills and real-world AI application concepts.
PAN India Reach
Training reach across India for students, professionals and organisations.
ISO 9001:2015 Certified Company
Quality-focused training organisation as highlighted in the course brochure.
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Frequently Asked Questions
Common questions about the Generative AI & Agents program.
What is the duration of the Generative AI & Agents program?
The brochure lists the program duration as 3 months with 60 hours of training.
Is the training conducted offline?
Yes. The brochure lists Offline as the mode of training.
What topics are covered in this course?
The curriculum covers AI and Generative AI, machine learning, data preparation, deep learning, neural networks, transformers, prompt engineering, RAG, AI automation, AI agents, tool calling, FastAPI and deployment workflows.
Does the course cover Large Language Models (LLMs)?
Yes. Large Language Models are included in the Generative AI curriculum along with embeddings, attention and transformer architecture.
Will I learn about AI agents?
Yes. The curriculum includes AI Agents Fundamentals, Tool Calling & Agent Workflows and chatbots with memory.
Does the course include RAG and vector databases?
Yes. The syllabus includes Retrieval-Augmented Generation, Vector Databases & Semantic Search, along with tools such as Chroma and FAISS.
Is prompt engineering included?
Yes. Prompt Engineering Masterclass is included under the AI Automation & Agents section.
Which AI tools and frameworks are covered?
The brochure lists tools and technologies including LangChain, LangSmith, LangGraph, LlamaIndex, n8n, Hugging Face, Chroma, TensorFlow, PyTorch, Streamlit, FastAPI, Docker, Postman, ChatGPT, Claude, FAISS and VS Code.
Does the program include practical projects?
Yes. The brochure specifically includes an OpenAI integration chatbot project and practical modern AI tools and automation topics.
Will I learn AI automation using n8n?
Yes. n8n Fundamentals, workflow concepts, API basics and AI content automation are included in the curriculum.
Does the course cover deployment of AI applications?
Yes. The curriculum includes FastAPI for AI Applications and Deployment & Production Workflows.
What are the current fees and batch timings?
The provided brochure does not specify current fees or batch timings. Please contact NIPSTec for the latest fee, batch schedule and admission details.