Students explore the exciting world of Artificial Intelligence and Machine Learning! They learn how machines think, make decisions, and recognize patterns using real-world data. With hands-on projects, they build smart systems like chatbots, image recognizers, and more—no advanced math required!
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Module 1: Introduction to AI & ML Concepts
What is AI? What is Machine Learning?
Real-world examples (chatbots, recommendation systems, image recognition)
Types of AI: Narrow vs. General
Types of ML: Supervised, Unsupervised, Reinforcement
Module 2: Python for Machine Learning
Recap of Python basics (variables, loops, functions)
Introduction to libraries: NumPy, Pandas, Matplotlib
Working with datasets
Data preprocessing: cleaning and preparing data
Module 3: Building Basic ML Models
Introduction to Scikit-learn
Building simple models: Linear Regression & Classification
Splitting data: training vs testing
Evaluating model performance (accuracy, confusion matrix)
Module 4: AI in Action – Final Project
Choose a real-world problem (e.g., predicting student scores, recognizing digits)
Train and test a basic ML model
Visualize and present results
Discuss ethical use of AI and its future