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Artificial Intelligence & Generative AI Course
- ✅ Join the Best AI Training Institute to master Python, SQL, Data Analysis & Statistics.
- ✅ Learn Machine Learning, Deep Learning & Generative AI with industry-relevant concepts.
- ✅ Master OpenAI, Gemini & Claude APIs to build powerful AI applications.
- ✅ Learn LangChain, RAG & AI Agents for next-generation AI development.
- ✅ Build AI applications using FastAPI & Full Stack AI Development.
- ✅ Gain practical experience with Cloud Deployment & real-world AI projects.
- ✅ Get complete Interview Preparation, Resume Building & Career Support to become job-ready.
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Become Job-Ready in AI & Generative AI
Offering Online and Offline Gen. AI Training Course in Chennai & Bangalore
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Overview of AI Course
AI Course – Overview
Our AI Training Program is designed to help students and working professionals build practical skills for today's AI-driven industry. Starting with Python and SQL, the course takes you through data analysis, statistics, machine learning, deep learning, Generative AI, and modern AI application development. You'll gain hands-on experience with OpenAI, Gemini, Claude, LangChain, RAG, AI Agents, FastAPI, Full Stack AI Development, and Cloud Deployment while working on practical projects and preparing for real-world job opportunities.
What You'll Learn From AI Training
- ➤Build a strong foundation in Python Programming and learn how to apply it to AI development.
- ➤Work with SQL and Databases to store, manage, and retrieve data efficiently.
- ➤Learn Data Analysis and Statistics to understand data and make meaningful insights.
- ➤Develop practical skills in Machine Learning and Deep Learning to solve real-world problems.
- ➤Explore Generative AI and understand how modern AI applications are developed.
- ➤Work with OpenAI, Gemini & Claude APIs to integrate AI capabilities into applications.
- ➤Learn LangChain and RAG to build AI applications that can work with custom and business-specific data.
- ➤Understand AI Agents and how autonomous AI workflows can be designed and implemented.
- ➤Develop scalable AI backends using FastAPI and create complete Full Stack AI Applications.
- ➤Learn how to deploy AI applications on cloud platforms and understand basic production practices.
- ➤Gain hands-on experience through real-world projects designed around practical business use cases.
- ➤Prepare for AI job opportunities with interview preparation, resume guidance, coding practice, and career support.
Hands-On Learning
Gain practical experience with Python, OpenAI, Gemini, Claude, LangChain, RAG, AI Agents, FastAPI, Full Stack AI Development, Cloud Deployment, and real-world AI projects.
Career Preparation
Prepare for AI job opportunities with interview preparation, resume guidance, coding practice, GitHub portfolio development, mock interviews, and placement assistance.
Career Opportunities
AI EngineerGenerative AI DeveloperMachine Learning EngineerLLM EngineerPrompt EngineerAI Automation EngineerData AnalystJunior Data Scientist
Become a Job-Ready AI & Generative AI Professional
12–16 Weeks • Students, Engineers, Analysts & Professionals • Intermediate → Advanced • Online / Classroom • Weekday & Weekend Batches
Program Benefits
- ✓ Hands-on Practical Training
- ✓ Real-Time Industry Projects
- ✓ Resume Building Support
- ✓ Mock Interviews & Placement Assistance
Key Technologies
PythonNumPyPandasNLPTransformersLLMsOpenAILangChain
Advanced AI Stack
LangGraphCrewAIAutoGenMCPRAGFAISSPineconeChroma
Development & Deployment
- AI Agents & Multi-Agent Systems
- LLMOps
- FastAPI & Streamlit
- Docker, AWS & Azure
Artificial Intelligence and Generative AI Training Syllabus
Curriculum Path: Python → NLP → Transformers → LLMs → LangChain → RAG → Agentic AI → LLMOps → Deployment
Key Technologies: Python | NumPy | Pandas | NLP | Transformers | LLMs | OpenAI | LangChain | LangGraph | CrewAI | AutoGen | MCP | RAG | FAISS | Pinecone | Chroma | AI Agents | LLMOps | FastAPI | Streamlit | Docker | AWS | Azure
Python · Module 1
Python Basics
- ●What is Python & setup (Anaconda, VS Code, Jupyter)
- ●Variables, Data Types
- ●Input / Output
- ●Operators
- ●Conditional statements (if, elif, else)
- ●Loops (for, while)
Python · Module 2
Data Structures
- ●Lists, Tuples, Sets, Dictionaries
- ●List comprehension
- ●Nested structures
Python · Module 3
Functions & OOP
- ●Functions, *args, **kwargs
- ●Lambda functions
- ●Classes & Objects
- ●Inheritance, Encapsulation, Polymorphism
Python · Module 4
File Handling & Exception Handling
- ●Read/Write files
- ●Try-except blocks
- ●Logging basics
Python · Module 5
Libraries for AI
- ●NumPy
- ●Pandas
- ●Matplotlib / Seaborn
- ●Basic EDA
Python · Module 6
APIs & Automation
- ●Requests library
- ●REST API basics
- ●JSON handling
Python · Module 7
Memory, Threading & Testing
- ●Memory management
- ●Multi Threading
- ●Logging
- ●Pytest
- ●Mini Projects
Gen AI · Module 1
NLP Foundations
- ●Text preprocessing
- ●Tokenization
- ●Stopwords
- ●Stemming / Lemmatization
Gen AI · Module 2
Text Representation
- ●Bag of Words
- ●TF-IDF
- ●Word2Vec / Embeddings
Gen AI · Module 3
Deep Learning Basics for NLP
- ●RNN, LSTM (conceptual)
- ●Attention mechanism
- ●Transformers
Gen AI · Module 4
Large Language Models (LLMs)
- ●What are LLMs
- ●GPT, Claude, LLaMA, Gemini overview
- ●Prompt Engineering
- ●Zero-shot / Few-shot learning
Gen AI · Module 5
LangChain
- ●Chains
- ●Prompts
- ●Memory
- ●Tools
- ●Agents (intro)
Gen AI · Module 6
Vector Databases
- ●Embeddings
- ●FAISS / Pinecone / Chroma
- ●Similarity search
Gen AI · Module 7
RAG
- ●What is RAG
- ●Chunking strategies
- ●Embedding + Retrieval
- ●Building Q&A system
Gen AI · Module 8
Advanced Gen AI
- ●Fine-tuning basics
- ●Open-source models
- ●Multimodal AI
- ●Guardrails
Gen AI · Projects
Generative AI Projects
- ●Chatbot using OpenAI
- ●PDF Q&A (RAG system)
- ●Resume Analyzer
- ●Document Search Engine
Agentic AI · Module 1
Introduction to AI Agents
- ●What is Agentic AI
- ●Difference: LLM vs Agent
- ●Use cases (automation, workflows)
Agentic AI · Module 2
Agent Frameworks
- ●GitHub
- ●LangChain Agents
- ●LangGraph
- ●CrewAI
- ●AutoGen
- ●MCP (Model Context Protocol)
Agentic AI · Module 3
Tools & Tool Calling
- ●Function calling
- ●API integration
- ●Custom tools
Agentic AI · Module 4
Multi-Agent Systems
- ●Agent collaboration
- ●Task delegation
- ●Workflow orchestration
Agentic AI · Module 5
Memory & Planning
- ●Short-term vs long-term memory
- ●Planning agents
- ●Reflection loops
Agentic AI · Module 6
Agent + RAG Systems
- ●Combining RAG + Agents
- ●Autonomous Q&A systems
Agentic AI · Module 7
LLMOps
- ●Monitoring
- ●Evaluation
- ●Logging
- ●Cost optimization
Agentic AI · Module 8
Deployment
- ●Streamlit / FastAPI
- ●Docker basics
- ●Cloud (AWS / Azure)
Final Projects
Industry-Level AI Projects
- ●AI Customer Support Agent
- ●Multi-Agent Report Generator
- ●Autonomous Data Analyst
- ●AI Automation Workflow (email / ticket system)
Real-Time Projects
Business-Focused AI Projects
- ●AI Resume Analyzer
- ●AI Email Assistant
- ●AI Content Generator
- ●AI HR Assistant
- ●AI Test Case Generator
- ●Enterprise RAG Chatbot
- ●AI Recruitment Platform
- ●Multi-Agent Business Assistant
Career & Portfolio Preparation
Resume Building Support
Prepare your resume for AI, Generative AI, Machine Learning, LLM, Prompt Engineering, and AI Automation opportunities.
GitHub Portfolio Development
Build a project portfolio using Generative AI, RAG, AI Agents, multi-agent systems, and business-focused AI applications.
Mock Interviews & Placement Assistance
Practice interview preparation and coding while receiving career support for AI job opportunities.
Why Choose Our AI & Generative AI Course?
Hands-On Practical Training
Learn by working with Python, AI APIs, LangChain, RAG, AI Agents, FastAPI, Full Stack AI Development, and deployment workflows.
Real-Time Industry Projects
Build practical AI applications including RAG systems, AI assistants, recruitment tools, and multi-agent business solutions.
Modern AI Technology Stack
Work with Transformers, LLMs, OpenAI, LangGraph, CrewAI, AutoGen, MCP, vector databases, LLMOps, Docker, AWS, and Azure.
Career Support
Get resume building support, GitHub portfolio development, mock interviews, placement assistance, and interview preparation.
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Artificial Intelligence & Generative AI Course – Frequently Asked Questions
Find answers about AI training, Generative AI, Agentic AI, projects, deployment, and career support.
1.Who is this AI training program designed for?
The program is designed for students, engineers, analysts, and working professionals who want practical skills for today’s AI-driven industry.
2.How long is the Artificial Intelligence and Generative AI course?
The training duration is 12–16 weeks.
3.What is the course level?
The course is structured at an Intermediate to Advanced level.
4.Is the course available online and in the classroom?
Yes. The program is offered through Online and Classroom training with Weekday and Weekend batches.
5.Does the course start with Python?
Yes. The syllabus starts with Python basics, data structures, functions, OOP, file handling, exception handling, AI libraries, APIs, automation, memory management, multithreading, logging, Pytest, and mini projects.
6.Will I learn SQL, Data Analysis, and Statistics?
Yes. The program includes SQL and Databases along with Data Analysis and Statistics as part of the AI learning path.
7.Are Machine Learning and Deep Learning covered?
Yes. The course covers Machine Learning and Deep Learning concepts and also includes NLP, RNN, LSTM, attention mechanisms, and Transformers.
8.Which Generative AI models and APIs are covered?
The course includes GPT, Claude, LLaMA, and Gemini concepts, along with OpenAI, Gemini, and Claude APIs.
9.Does the course cover Prompt Engineering?
Yes. Prompt Engineering, Zero-shot learning, and Few-shot learning are included in the Large Language Models module.
10.Will I learn LangChain and RAG?
Yes. The curriculum covers LangChain chains, prompts, memory, tools, agents, vector databases, embeddings, similarity search, chunking strategies, retrieval, and building RAG-based Q&A systems.
11.Which vector databases are included?
FAISS, Pinecone, and Chroma are included as part of the Vector Databases and RAG learning path.
12.Does the course include Agentic AI?
Yes. The Agentic AI section covers AI agents, agent frameworks, tool calling, multi-agent systems, memory and planning, Agent + RAG systems, LLMOps, and deployment.
13.Which Agentic AI frameworks are covered?
The syllabus includes LangChain Agents, LangGraph, CrewAI, AutoGen, and MCP (Model Context Protocol), along with GitHub.
14.Does the program include LLMOps?
Yes. LLMOps topics include monitoring, evaluation, logging, and cost optimization.
15.Will I learn AI application deployment?
Yes. Deployment topics include Streamlit, FastAPI, Docker basics, and cloud deployment using AWS and Azure.
16.What projects are included in the Generative AI section?
Projects include an OpenAI chatbot, PDF Q&A RAG system, Resume Analyzer, and Document Search Engine.
17.What final Agentic AI projects are included?
Final projects include an AI Customer Support Agent, Multi-Agent Report Generator, Autonomous Data Analyst, and AI Automation Workflow for email or ticket systems.
18.What real-time projects are included?
Real-time projects include an AI Resume Analyzer, AI Email Assistant, AI Content Generator, AI HR Assistant, AI Test Case Generator, Enterprise RAG Chatbot, AI Recruitment Platform, and Multi-Agent Business Assistant.
19.What career opportunities can I explore after this training?
Career opportunities listed for the program include AI Engineer, Generative AI Developer, Machine Learning Engineer, LLM Engineer, Prompt Engineer, AI Automation Engineer, Data Analyst, and Junior Data Scientist.
20.What career support is included?
The program benefits include Resume Building Support, GitHub Portfolio Development, Mock Interviews, and Placement Assistance.
Comprehensive AI & Generative AI Curriculum
Learn Python, SQL, Data Analysis, Statistics, Machine Learning, Deep Learning, NLP, Transformers, LLMs, Generative AI, RAG, and Agentic AI.
Generative AI Application Development
Build AI applications using OpenAI, Gemini, Claude, LangChain, vector databases, RAG, FastAPI, and Full Stack AI Development.
Agentic AI & LLMOps
Learn LangGraph, CrewAI, AutoGen, MCP, tool calling, multi-agent systems, memory, planning, monitoring, evaluation, logging, and cost optimization.
Projects, Deployment & Career Support
Work on real-time AI projects, deploy with Streamlit, FastAPI, Docker, AWS and Azure, and get resume, GitHub portfolio, mock interview and placement support.
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