CLASS 9Foundation LEVEL

Artificial Intelligence & Machine Learning

This course builds on foundational AI awareness and introduces Class 9 students to the actual mechanics of Machine Learning — including how models are trained, what datasets are, and how to build a simple ML model using no-code tools. Students also explore how AI is applied across real industries.

Duration

10 Days

Total Hrs

20 Hours

Level

Foundation

What You'll Get

Expert Trainer-Led Sessions
Training Slides & Learning Materials
Practical Assignments
Assessment & Progress Report
Course Completion Certificate

Methodology

Instructor-Led Training
Practical Hands-on Sessions
Live Demonstrations
Mini Assignments & Assessments
Project-Based Learning

Detailed Syllabus

Practical, engaging and concept-based learning

1

The AI & ML Landscape

Topics Covered

AI vs ML vs Deep Learning vs GenAI — clear distinctions
Types of AI: Narrow, General, Super AI
AI applications across industries

Practical / Activities

Case study analysis of 3 real AI applications

Outcomes

Distinguish between AI subfields clearly
Identify industry use cases of AI

2

Understanding the Machine Learning Workflow

Topics Covered

Ready to start?

₹499

One-time Enrollment Fee

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Course Snapshot

Course Code
LEQ-C9-AI-ML
Grade Level
Class 9

Prerequisites

Basic computer literacy

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What is a dataset? Rows, columns, and features
Supervised vs Unsupervised learning
Introduction to regression and classification

Practical / Activities

Explore and interpret a simple CSV dataset

Outcomes

Explain the end-to-end ML pipeline
Read and interpret basic datasets

3

Building a No-Code ML Model

Topics Covered

Introduction to no-code ML platforms
Training a simple image/gesture classifier
Testing and evaluating model accuracy

Practical / Activities

Build a classification model using Teachable Machine (or similar)

Outcomes

Build a working ML model without code
Evaluate a model's basic accuracy

Class 8 AI awareness recommended but not required

Objectives

Understand the complete ML workflow: data, training, prediction
Differentiate supervised and unsupervised learning
Work with simple datasets
Build a basic ML model using no-code tools