Learn In தமிழ் Certification in Agentic AI Program (Tamil)
4.8K+ students have already registered
Why choose this course?
Prestigious certification from Vishlesan i-Hub, IIT Patna
Real world capstone projects designed to solve practical challenges
Delivery in Tamil, learn from experts who speak your language
Placement assistance for 1 year, plus resume review and interview coaching sessions facilitated by Masai. (Eligibility: 7+ CGPA and 65% attendance)
Expert faculty and industry insights
*Certificate Eligibility: Minimum 65% overall attendance and 30% marks in cumulative evaluations score
What will you learn?
Go from zero to job-ready in AI/ML with beginner-friendly, industry-aligned curriculum from I-Hub IIT Patna, taught in Tamil — covering Python, data analysis, machine learning, and generative AI with hands-on projects. Built for beginners, designed to get you building real things fast.
Meet our Instructor Team
Abhinandan S P
Asst. Professor at IIT Palakkad
Venkatesh
Senior Machine Learning Engineer | PayPal
Aswath Rao V S
AI / ML Engineer at Ford Motors, Ex- PayPal and Pine Labs
Abhinandan S P
Asst. Professor at IIT Palakkad
Venkatesh
Senior Machine Learning Engineer | PayPal
Aswath Rao V S
AI / ML Engineer at Ford Motors, Ex- PayPal and Pine Labs
*Note: Instructors teaching your batch may change based on specific module expertise, or circumstances such as personal emergencies, unavailability etc.
Course Details
Foundations of Data
- Learn Python from the basics - variables, loops and functions
- Write SQL queries that filter, sort and join across millions of rows
- Turn messy data into analysis-ready datasets with NumPy and Pandas
- Explore data to find the patterns and outliers, then show them in charts
Mini Projects Unlocked
CryptoWatch — Real-Time Market Sentiment Tracker
FoodSafety Insights — Restaurant Inspection Intelligence
TrendPulse — What's Actually Trending Right Now
Classical ML
- Frame a business problem as a prediction, classification or segmentation task
- Build predictive models - linear and logistic regression, decision trees and random forests
- Validate every model with train-test splits and cross-validation checks
- Explain predictions and feature importance in plain business language
Mini Projects Unlocked
ChurnGuard — Predicting Customer Exit Before It Happens
LoanLens — Automated Credit Risk Assessment
RentRight — Predicting Fair Apartment Prices
GenAI & Agents
- Learn how large language models work - tokens, context and prompting
- Turn a single prompt into structured, reliable JSON output
- Build RAG pipelines that retrieve real answers from your own documents
- Design AI agents that plan and call tools - then deploy them as a working app
Mini Projects Unlocked
DataGenie — Natural Language Data Analyst
SupportAI — Intelligent FAQ Helpdesk Agent
ResearchPilot — Automated Market Research Agent
English & Soft Skills Training
Build the everyday English you need to speak up with confidence
Form clear, correct sentences in speech and in writing
Speak with impact - tone, pace and reading the room
Handle interviews, emails and workplace conversations professionally
Please Note :- "Due to the evolving nature of the industry expectations and partner institute feedback, some syllabus aspects may change. Any updates will be communicated during the Inauguration Session(s) or at the start of the relevant module"
Capstone Project
Nykaa Review Radar
Nykaa sells thousands of beauty products, and every month someone decides which to promote, discount or drop on pure instinct. Nobody has time to read a hundred thousand reviews, so by the time a rating visibly collapses it is too late to act.
What you'll build:
An early warning system that cleans the full review corpus into SQL and trains a model to flag products heading for a bad rating before it happens. On top sits a chat agent with a database tool, a model tool and retrieval over review text, shipped as a FastAPI service on Hugging Face Spaces.
Final Outcome
A monthly action list ranking the top 200 products as promote, hold, discount or remove. Each row carries a risk score and the real customer quotes behind it, plus a live demo link you can share.
Zerodha Market Pulse
An investor sees a stock down four percent and cannot tell if it is normal noise or the start of trouble. Finding out means reading years of filings and a week of news, so most people either panic or ignore it.
What you'll build:
A Nifty 50 assistant analyzes five years of price history to classify stocks as calm, normal, or turbulent for the upcoming month, focusing on risk conditions. It uses price and document retrieval tools, orchestrated in LangGraph and deployed on Render, explaining each call with the source.
Final Outcome
A weekly watchlist tagging every Nifty 50 stock with its risk regime and a confidence score. Each call comes with the specific news or disclosure driving it, so it can be checked rather than trusted blindly.