Advance Certificate Programme in AI, Machine Learning, and Agentic AI

Program Description

•    Lead AI/ML initiatives: Drive innovation and solve complex business problems
•    Make data-driven decisions: Use AI to extract meaningful insights from data
•    Collaborate with AI/ML teams: Effectively communicate with data scientists and engineers
•    Stay ahead of the curve: Keep up with the latest advancements in AI and ML

Key Highlights

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Unique Two-Phase Programme Design: Taught in two parts: Foundational AI ML and cutting-edge agentic AI applications

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Learn from IIT and IITM Pravartak Faculties: Video lectures and select masterclasses by IIT faculty; few masterclasses by IITMP guest faculty

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Save 17% More With an Integrated AI Curriculum: Eliminates the need for multiple courses, saving INR 45,000

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9 Certifications for Global Recognition: Earn 3 IITM Pravartak certificates and 6 IBM industry credentials

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Highest Number of Tools and Libraries Across AIML Courses: 40+ latest tools and libraries such as TensorFlow, Keras, Scikit-Learn and more

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Advanced Projects for Higher Practical Readiness: 30+ real-world AI projects that build serious practical skills

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GitHub Portfolio and Kaggle Set Up: Get started on GitHub and Kaggle through programme projects

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Emeritus Career Services Support: Enhance your career prospects with Emeritus Career Services and an IIMJobs Pro membership

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Latest Industry Research Papers: Dive into real-world studies for in-depth insights

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Optional Immersion at IIT Madras Research Park: Visit the IIT Madras Research Park, connect with like-minded AI and ML enthusiasts

Learning Format

Online

Duration

15 Months

Certified by

IITM Pravartak Technologies Foundation
Technology Innovation Hub (TIH) of IIT Madras and
Emeritus

Program Fee

₹2,10,000 + GST

Education Qualification

Minimum Graduate (10+2+3); Diploma Holders with min. 5 years of work experience (Math and Programming knowledge is required)

Teaching Hours

Dual-Phase Programme

Lead Faculty

Prof. C Chandra Shekar
Professor, IIT Madras

- Ph.D. Degree in Computer Science and Engineering from IIT Madras
- M.Tech. Degree in Electrical Engineering from IIT Madras

Professor C. Chandra Sekhar is a distinguished faculty member in the Department of Computer Science and Engineering at IIT Madras. He was the Head of Department of the CSE Department at IIT Madras from 2019 to 2022. His expertise spans speech recognition, neural networks, kernel methods, machine learning, deep learning, and metric learning. A highly respected researcher, Prof. Sekhar has authored numerous papers featured in prestigious national and international peer-reviewed journals.

Prof. Dileep A. D.
Professor at IIT Dharwad

- Ph.D. Degree in Computer Science and Engineering from IIT Madras
- M.Tech. Degree in Computer Science and Engineering from IIT Madras

Dr. Dileep A. D. is a Professor at IIT Dharwad. He has 10+ years of teaching experience across institutions like IIT Madras, IIT Mandi, and IIT Dharwad. He is widely recognised for his expertise in pattern recognition, kernel methods, machine learning, speech technology, and computer vision. He earned both his M.Tech and PhD in Computer Science and Engineering from IIT Madras, Chennai. A prolific researcher, Dr. Dileep has contributed extensively to the field, with numerous publications in prestigious peer-reviewed journals.

Prof. Madhusudhanan Baskaran
Lead Faculty, IITM Pravartak

- Ph.D. Degree in Wireless Sensor Network with Artificial Intelligence from Anna University
- M.Tech. Degree in Computer Science and Engineering from M.Kumarasamy College OF Engineering

Prof. Madhusudhanan is a Principal AIML Consultant and Lead Faculty at IITM Pravartak, specialising in Agentic AI, generative systems, and intelligent automation. With over 32 years of experience across academia, industry, and government, he brings deep expertise in designing AI agents that plan, reason, and adapt autonomously.

His portfolio includes AI-led projects for the Supreme Court of India, CAG, ReBIT, and the Indian Army, focusing on responsible, scalable AI systems using LLMs, speech technologies, and document intelligence. A key voice in India’s AI transformation, he actively mentors deep-tech startups and leads initiatives in explainable AI and modular agent architectures.

Learning Module

Python Preparatory Session

Module 1: Introduction to the Programme

Module 2: Mathematics Fundamentals 

Module 3: Python Fundamentals 

Module 4: Exploratory Data Analysis 

Module 5: Applications of AI and ML 

Module 6: Paradigms of Machine Learning 

Module 7: Regression Methods 

Module 8: Probabilistic Models for Classification 

Module 9: Support Vector Machines for Classification

Module 10: Dimension Reduction Techniques

Module 11: Decision Trees

Module 12: Ensemble Methods

Module 13: Clustering Techniques

Module 14: Multilayer Feedforward Neural Networks for Classification and Regression

Module 15: Deep Feedforward Neural Networks

Module 16: Convolutional Neural Networks

Module 17: Recurrent Neural Networks 

Module 18: Generative Adversarial Networks 

Module 19: Transformers 

Module 20: Applications of Generative AI 

Module 21: Reinforcement Learning

Module 22: Capstone Project Optional

Module: Deployment of MLOps Optional

Module: Cloud Deployment

Module 1: Getting Started with Python and ChatGPT

Module 2: Data Types, Variables and Control Flow

Module 3: Functions and Working with Libraries

Module 4: Fundamentals of AI and ML

Module 5: Large Language Models (LLMs)

Module 6: Embedding Models and Vector Basics

Module 7: Agentic Tools in Python

Module 8: Introduction to Agentic AI

Module 9: Programming and Frameworks for Agentic Systems

Module 10: Agent Architectures and Collaboration

Module 11: Decision-Making and Planning in Agents

Module 12: Memory and Knowledge Retrieval in Agents with MCP

Module 13: Prompt Engineering and Adaptive Instructions

Module 14: Learning and Adaptation in Agents

Module 15: Advanced Retrieval-Augmented Generation (RAG)

Module 16: Deploying and Monitoring Agentic Systems

Module 17: Agent Evaluation and Debugging

Module 18: Ethics, Safety and Governance in Agentic AI

Module 19: Real-World Applications and Case Studies

Module 20: Low-Code Tools Deep Dive

Module 21: Capstone Project—Build Your Own Agent



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