Syllabus

• AI basics: what AI/ML is + real-world examples
• Python basics for AI/ML (NumPy, Pandas)
• Data collection + cleaning + preprocessing
• EDA: visualization + insights
• Supervised learning: Regression
• Supervised learning: Classification
• Model evaluation: accuracy, precision/recall, F1, ROC-AUC
• Overfitting + regularization + cross-validation
• Unsupervised learning: clustering + PCA
• Intro to neural networks (MLP) + deep learning basics
• Mini project: dataset → model → report
• Ethics, bias, privacy, responsible AI + deployment basics