AI and Machine Learning Masterclass at IIIT Delhi

Build a strong foundation in artificial intelligence and machine learning

This masterclass is designed for students, professionals, tech enthusiasts, and aspiring data scientists who want to dive into the world of artificial intelligence and machine learning.

You’ll learn core concepts, real-life applications, and get hands-on with ML workflows – even if you’re just starting out. Whether you’re from a coding, non-coding, or business background, this workshop will help you make sense of AI and ML in a practical, beginner-friendly way.

This workshop is for:

1) Students and graduates exploring careers in AI/ML

2) Non-tech professionals curious about how AI works

3) Aspiring data scientists wanting hands-on experience

4) Educators and trainers looking to adopt AI tools

5) Working professionals aiming to apply ML in their field

6) Anyone eager to understand AI’s real-world impact

What You’ll Learn

Audience Introduction

Timing: 10:30 a.m. – 11:00 a.m.

Welcome and expectations

AI/ML landscape overview

Real-world impact of machine learning

Understanding AI and ML Fundamentals

Module 1

Timing (11:00 a.m. to 1:00 p.m.)

Start with core concepts and how machines learn from data.

Topics Covered

* What is AI and ML?

Definitions, differences, and real-life applications

* Supervised vs Unsupervised Learning

Use cases, examples, and simplified explanation

* Workflow of a Machine Learning Model

Data -> Training -> Evaluation -> Prediction

* Common ML Algorithms (explained simply)

Linear regression, classification, clustering

Skills Acquired

* AI and ML conceptual clarity

* Data-driven thinking

* Model building mindset

* Application awareness

Tools and Techniques in Machine Learning

Module 2

Timing (2:00 p.m. to 3:30 p.m.)

Get familiar with tools used to build and experiment with AI and ML.

Topics Covered

* Hands-on with Google Teachable Machine

Create a basic ML model in minutes

* Intro to Python and Scikit-learn

Beginner-friendly ML Libraries

* Role of Data in Machine Learning

Collecting, cleaning, and splitting datasets

* Responsible AI

Bias, fairness, and ethical use of data

Skills Acquired

* Tool-based learning

* Awareness of Python ML libraries

* Responsible AI principles

* Understanding ML pipelines

Mini Project and AI Career Insights

Module 3

Timing (3:30 p.m. to 05:30 p.m.)

Apply what you’ve learned and plan your next steps in the AI/ML journey.

Topics Covered

* ML Mini Project (live activity)

Build a model using Google Teachable Machine or Scikit-learn

* How to Learn AI/ML Further

Certification paths, courses, online platforms

* AI Career Roles and Salary Trends

Data analyst, ML engineer, AI consultant

* What’s Next in AI?

GenAI, LLMs, AI in your field

Skills Acquired

* Hands-on ML model creation

* Career mapping in AI/ML

* Project presentation and teamwork

* Clarity on next steps