CLASS 10Intermediate LEVEL

Artificial Intelligence & Machine Learning

A hands-on Machine Learning course for Class 10 students using Python and Scikit-learn. Students learn to preprocess data, build regression and classification models, and evaluate model performance — developing the practical skills needed for board-level computer science and future specialization.

Duration

10 Days

Total Hrs

24 Hours

Level

Intermediate

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

AI Careers & Industry Landscape

Topics Covered

AI/ML career pathways and required skillsets
Current trends: multimodal AI, agentic systems
Applied distinctions: AI vs ML vs DL vs GenAI

Practical / Activities

Industry mapping exercise: AI roles and skills

Outcomes

Understand career pathways in AI/ML
Map current industry trends

2

Data Preprocessing & Exploration

Topics Covered

Ready to start?

₹499

One-time Enrollment Fee

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Course Snapshot

Course Code
LEQ-C10-AI-ML
Grade Level
Class 10

Prerequisites

Basic Python programming knowledge recommended

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Working with Pandas: loading and cleaning data
Handling missing values
Data visualization with Matplotlib

Practical / Activities

Clean and visualize a real-world sample dataset

Outcomes

Preprocess raw data for ML use
Visualize data trends and patterns

3

Building ML Models with Scikit-learn

Topics Covered

Linear regression — theory and implementation
Logistic regression for classification
Introduction to decision trees

Practical / Activities

Build a linear regression model
Build a basic classification model

Outcomes

Implement regression and classification models
Apply Scikit-learn to real problems

4

Model Evaluation

Topics Covered

Accuracy, precision, and recall (introductory)
Confusion matrix — reading and interpreting
Train-test split methodology

Practical / Activities

Evaluate a trained classification model using a confusion matrix

Outcomes

Evaluate ML model performance
Interpret standard evaluation metrics

Class 9 ML foundations recommended

Objectives

Build ML models using Python and Scikit-learn
Understand and apply data preprocessing techniques
Train and evaluate regression and classification models
Interpret model evaluation metrics