Advanced Certificate in Machine Learning, Gen AI & LLMs for BA

Programme Curriculum

Module 1: Fundamentals of Machine Learning with Practical Applications

Introduction to Machine Learning

Overview of Machine Learning Concepts

Supervised, Unsupervised, and Reinforcement Learning
Common Machine Learning Algorithms

Machine Learning Workflow

Problem Definition and Data Collection
Data Preprocessing and Feature Engineering
Model Selection and Training
Model Evaluation and Tuning

Python Tools for Machine Learning

Introduction to Scikit-Learn

Data Preprocessing with Scikit-Learn
Implementing Classification and Regression Models
Model Evaluation Metrics and Cross-Validation

Advanced Machine Learning Techniques

Ensemble Methods: Random Forests, Gradient Boosting
Dimensionality Reduction: PCA, LDA
Clustering Techniques: K-Means, Hierarchical Clustering

Practical Applications of Machine Learning

Predictive Modeling with Real-World Datasets
Implementing Recommendation Systems
Time Series Forecasting with Machine Learning Models

Capstone Project: Machine Learning Application

Problem Statement and Data Exploration
Model Development and Evaluation
Optimization and Final Presentation

Module 2: Deep Learning Technologies with Practical Python Tools and Frameworks

Introduction to Deep Learning

Fundamentals of Neural Networks

Neurons, Layers, and Activation Functions
Loss Functions and Optimization

Deep Learning Architectures

Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Advanced Architectures: LSTMs, GRUs, Attention Mechanisms

Python Tools for Deep Learning

Introduction to TensorFlow and Keras

Building Neural Networks with Keras
Training and Evaluating Deep Learning Models
Visualizing Model Performance

PyTorch for Deep Learning

Understanding Tensors and Autograd
Implementing CNNs and RNNs with PyTorch
Transfer Learning and Fine-Tuning Pre-Trained Models

Practical Applications of Deep Learning

Image Classification and Object Detection with CNNs
Sequence Modeling for NLP Tasks with RNNs
Advanced Topics: GANs, Autoencoders, and Attention Models

Capstone Project: Deep Learning Application

Problem Statement and Data Preparation
Model Development and Tuning
Final Model Evaluation and Presentation

Module 3: Generative AI Technologies with Python Tools and Frameworks

Introduction to Generative AI

Overview of Generative Models

GANs, VAEs, and Diffusion Models
Applications of Generative AI

Generative Adversarial Networks (GANs)

GAN Architecture and Training Process
Implementing GANs with TensorFlow/PyTorch
Applications of GANs: Image Synthesis, Style Transfer

Variational Autoencoders (VAEs) and Diffusion Models

Understanding VAEs

Encoder-Decoder Architecture
Latent Space Representation and Sampling

Diffusion Models for Generative AI

Basics of Diffusion Processes
Implementing Diffusion Models in Python

Practical Applications of Generative AI

Creative AI: Art and Music Generation
Data Augmentation with Generative Models
Ethical Considerations in Generative AI

Capstone Project: Generative AI Application

Project Setup and Data Collection
Model Development and Fine-Tuning
Final Model Deployment and Presentation

Module 4: Large Language Models (LLMs), Fine-Tuning, Agents, and RAG

Introduction to Large Language Models (LLMs)

Evolution of LLMs: From GPT to GPT-4 and Beyond
Transformer Architecture and Attention Mechanism
Pre-trained vs. Fine-Tuned LLMs: Differences and Use Cases

Fine-Tuning LLMs

Data Preparation for Fine-Tuning
Fine-Tuning LLMs with Hugging Face Transformers
Evaluating and Optimizing Fine-Tuned Models

Building Custom AI Agents with LangChain

Introduction to LangChain for Building AI Agents
Creating Custom Chains and Workflow Automation
Integrating LLMs with External APIs

Retrieval-Augmented Generation (RAG)

Understanding RAG Concepts and Architecture
Implementing RAG with Hugging Face Transformers
Practical Applications of RAG in QA and Search Systems

Capstone Project: LLM and RAG Application

Project Setup and Data Collection
Model Development and Fine-Tuning
Final Model Evaluation and Presentation

Module 5: Applications of LLMs with Text, Video, Image, and Audio

Text-Based Applications of LLMs

Automated Content Creation and Summarization
Building Conversational Agents and Chatbots
Sentiment Analysis and Text Classification

Image and Video Applications with LLMs

Text-to-Image Generation with DALL-E and CLIP
Video Synthesis and Editing with Generative Models
Combining LLMs with Computer Vision Tasks

Audio Applications of LLMs

Text-to-Speech (TTS) Systems with LLMs
Music and Sound Generation
Voice Cloning and Audio Enhancement

Capstone Project: Multimodal LLM Application

Project Proposal and Planning
Data Collection and Model Development
Final Integration and Presentation

Module 6: Building Integrated Generative AI Applications

Multimodal Generative AI Systems

Integrating Text, Image, and Audio Models
Case Studies: AI Art, Music Videos, Virtual Worldst
Developing Cross-Modal Retrieval Systems

Customizing and Extending Generative AI Models

Fine-Tuning LLMs for Multimodal Tasks
Building End-to-End Multimodal Pipelines
Deployment Strategies for Multimodal Applications

Capstone Project: Integrated Generative AI Application

Project Planning and Data Collection
Model Integration and Workflow Design
Final Testing, Optimization, and Presentation

Module 7: Module 7 Productionizing the Applications – End-to-End Pipeline

Introduction to Productionizing AI Models

Overview of MLOps and Best Practices
Setting Up CI/CD Pipelines for AI Applications
Monitoring and Maintaining AI Models in Production

Deploying AI Models with Python Tools

Model Deployment with TensorFlow Serving and TorchServe
Scaling Applications with Docker and Kubernetes

End-to-End Pipeline for LLM and Generative AI Applications

Note: *subject to modifications at a later stage if required, as per the discretion of the faculty.

Fee Structure

Application Fee

(non-refundable)*

INR 1,500/- + GST

Programme Fee

INR 1,30,000/- + GST

Instalment	Instalment I	Instalment II	Instalment III
Amount	INR 60,000/- + GST As mentioned in the offer letter	INR 40,000/- + GST 5^th July 2026	INR 30,000/- + GST 5^th September 2026

Instalment

Instalment I

Instalment II

Instalment III

Amount

INR 60,000/- + GST

As mentioned in the offer letter

INR 40,000/- + GST

5^th July 2026

INR 30,000/- + GST

5^th September 2026

Instalment I

Amount

INR 60,000/- + GST

As mentioned in the offer letter

Instalment II

Amount

INR 40,000/- + GST

Due on 5^th July 2026

Instalment III

Amount

INR 30,000/- + GST

Due on 5^th September 2026

EASY EMI OPTIONS AVAILABLE*

FAQs

This programme is designed to equip professionals with a strong foundation in Machine Learning, Generative AI, and Large Language Models (LLMs), with a focus on real-world business applications and hands-on learning.

The programme is ideal for:

Working professionals and managers
Engineers and developers
Data analysts and data scientists
Technology leaders and consultants
Professionals transitioning into AI & GenAI roles

The curriculum covers:

AI & Machine Learning fundamentals
Deep Learning concepts
Generative AI models (GANs, VAEs)
LLMs & GPT-based applications
NLP, chatbots, image & speech processing
Emerging AI & ML trends

Yes. The programme includes practical assignments and hands-on projects, including text generation using GPT models, enabling participants to apply concepts to real business use cases.

The programme is delivered through structured online learning, combining expert-led sessions, practical exercises, and applied projects for maximum flexibility.

Basic knowledge of programming or data concepts is helpful, but the programme starts with foundational AI and ML concepts, making it suitable for both beginners and professionals with prior exposure.

Participants who successfully meet the evaluation criteria and satisfy the requisite attendance criteria will be awarded a 'Certification of Completion' - Advanced Certificate Programme in Machine Learning, Gen AI & LLMs for Business Applications.

Graduates can pursue roles such as:

AI / Machine Learning Engineer
Data Scientist
Generative AI / LLM Engineer
AI Researcher
AI Consultant

The programme helps professionals build future-ready AI capabilities, apply AI to business problems, and strengthen their profile with a recognised IIT Madras–backed certification.

Yes. The programme is designed with a business application focus, enabling professionals to understand how AI and Generative AI can drive innovation, efficiency, and competitive advantage.

Advanced Certificate Programme in Machine Learning, Gen AI & LLMs for Business Applications Programme offered by IITM Pravartak Technology Innovation Hub of IIT Madras

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Programme Overview

Programme Highlights

Programme Curriculum

Module 1: Fundamentals of Machine Learning with Practical Applications

Overview of Machine Learning Concepts

Machine Learning Workflow

Introduction to Scikit-Learn

Advanced Machine Learning Techniques

Module 2: Deep Learning Technologies with Practical Python Tools and Frameworks

Fundamentals of Neural Networks

Deep Learning Architectures

Introduction to TensorFlow and Keras

PyTorch for Deep Learning

Module 3: Generative AI Technologies with Python Tools and Frameworks

Overview of Generative Models

Generative Adversarial Networks (GANs)

Understanding VAEs

Diffusion Models for Generative AI

Module 4: Large Language Models (LLMs), Fine-Tuning, Agents, and RAG

Module 5: Applications of LLMs with Text, Video, Image, and Audio

Module 6: Building Integrated Generative AI Applications

Module 7: Module 7 Productionizing the Applications – End-to-End Pipeline

Cutting-Edge Tools and Techniques

Machine Learning Packages

Gen AI Packages

Models

Eligibility Criteria

What Your Career Will Look

Fee Structure

Application Fee

INR 1,500/- + GST

Programme Fee

INR 1,30,000/- + GST

Faculty & Industry Expert

Prof. Babji Srinivasan

Dr. Ramji Srinivasan

FAQs

Programme Certification