Artificial Intelligence (AI)

Dive into the world of Artificial Intelligence and learn how to build intelligent systems from scratch. This course provides hands-on experience in Python programming, large language models (LLMs), RAG (Retrieval-Augmented Generation), Agent AI, and advanced AI development techniques — preparing you for real-world AI projects and innovations.

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Course Pricing

Online:

₹10000 ₹6000

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What You'll Learn

Understand the fundamentals of AI and its real-world applications. Learn Python essentials for AI programming. Explore Large Language Models (LLMs) and practical use cases. Implement Retrieval-Augmented Generation (RAG) workflows. Build intelligent agents using Agent AI. Integrate FastAI for deep learning applications. Create AI-powered frontends and full-stack projects. Work on a complete end-to-end AI project.

Course Highlights

Expert-led instruction
Hands-on projects
Real-world case studies
Community support

Course Includes

Online real-time classes
Lifetime access
Certification of completion
Real-time doubt clearing
Full refund if you don’t understand the course

Who This Course Is For

Beginners interested in learning AI and automation
Developers looking to transition into Artificial Intelligence
Professionals seeking career advancement in data science and AI
Students aiming to build AI-driven applications
Enthusiasts who want to explore LLMs, RAG, and modern AI tools

Artificial Intelligence (AI)

Course Pricing

What You'll Learn

Course Highlights

Course Includes

Who This Course Is For

Course Modules

Week 1: Python for AI (Days 1–5)

Objective: Build strong Python foundations required for Artificial Intelligence

development.

Topics Covered:

Introduction to Python & Development Environment Setup

Conditional Statements (if, elif, else)

Loops (for loops, while loops)

Functions (parameters, return values, scope)

Object-Oriented Programming

Classes & Objects

Constructors, Methods, Inheritance

Hands-on Mini Tasks & Coding Exercises

Outcome:

Learners will be able to write modular, efficient Python code using AI-centric

programming practices.

Week 2: Retrieval Augmented Generation (RAG)

Objective: Prepare learners to integrate LLMs with private datasets securely.

Topics Covered:

What is RAG? Need & Advantages

Vector Databases Overview (FAISS / Pinecone / Chroma DB)

Text Embeddings & Document Chunking

Building a Basic RAG Pipeline

Data ingestion

Embedding generation

Retrieval

LLM response generation

Code Implementation Session:

RAG using Python+ OpenAI / Llama Models

Outcome:

Ability to build a simple chatbot that retrieves relevant information from custom

documents.

Week 3: AI Agents Development

Objective: Enable creation of autonomous AI systems that can interact, execute tasks

& make decisions.

Topics Covered:

What is an AI Agent?

Components: Memory, Tools, Planning, Actions

Frameworks overview: LangChain Hands-on:

Creating a simple task-oriented AI Agent

Adding tools like Search, Calculator, File Read/Write

Multi-Agent Collaboration

Outcome:

Learners will build task-automating AI agents capable of real-world workflows.

Week 4: End-to-End AI Project Implementation

Objective: Develop and deploy a complete AI solution with modern software

engineering practices.

Topics Covered:

AI Backend Development with Python + FastAPI

API Integration with LLM models

Frontend Development (React / HTML+CSS+JavaScript)

Final Capstone Project:

End-to-end AI Web App

Clean UI + fully connected backend AI system

Outcome:

Full-stack AI engineering capability: from Model Integration ? API creation ?

Frontend ? Deployment.

T. Senthil kumaran

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