CLASS 11, 12Advanced LEVEL

Artificial Intelligence & Machine Learning

An advanced, project-oriented Machine Learning course for Class 11 students covering feature engineering, ensemble methods, unsupervised learning, and model tuning. This course prepares students to independently design, build, and evaluate robust ML models — a strong stepping stone toward higher studies or specialization in Data Science and AI.

Duration

10 Days

Total Hrs

28 Hours

Level

Advanced

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

Advanced AI System Design Thinking

Topics Covered

Modern AI architecture overview
AI system design thinking
Emerging trends: AI agents, multimodal AI, edge AI

Practical / Activities

Technical comparison report of 3 modern AI system approaches

Outcomes

Understand modern AI system architecture
Think critically about ML system design

2

Feature Engineering

Topics Covered

Ready to start?

₹499

One-time Enrollment Fee

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Course Snapshot

Course Code
LEQ-C11-AI-ML
Grade Level
Class 11, 12

Prerequisites

Python programming proficiency

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Feature selection and transformation
Encoding categorical variables
Feature scaling and normalization

Practical / Activities

Engineer features for an improved model on a sample dataset

Outcomes

Apply feature engineering to boost model performance

3

Ensemble Methods & Model Tuning

Topics Covered

Random Forests — theory and application
Introduction to boosting techniques
Cross-validation and hyperparameter tuning (GridSearch)

Practical / Activities

Build and tune a Random Forest classifier

Outcomes

Build ensemble models
Tune models for improved accuracy

4

Unsupervised Learning & Evaluation

Topics Covered

Introduction to clustering (K-means)
Evaluation metrics: F1-score, ROC-AUC
Choosing the right model for a problem

Practical / Activities

K-means clustering mini-project on a sample dataset

Outcomes

Apply clustering techniques
Select and justify appropriate evaluation metrics

Class 10 ML course or equivalent experience

Objectives

Apply feature engineering techniques to improve model performance
Build ensemble models such as Random Forests
Perform hyperparameter tuning and cross-validation
Apply unsupervised learning techniques like clustering