أساسيات تعلم الآلة

Estimated duration: 32 hours (6 modules, 18 lessons + project)

What you will learn

• Explain what ML is, distinguish supervised, unsupervised and reinforcement learning, and frame a problem with features and labels
• Prepare data: train/test split, missing values, encoding and scaling, avoiding leakage
• Train and interpret linear regression and evaluate it with MAE, MSE, RMSE and R²
• Train classifiers and evaluate them with a confusion matrix, accuracy, precision and recall
• Explain overfitting, cross-validation, gradient descent, decision trees, k-NN and k-means
• Discuss fairness, privacy and limits of models and deliver an end-to-end project

Who it is for: secondary-3 and university learners entering AI; gateway to all ML/AI follow-up courses

Prerequisites: CRS-GEN-PYTHON; school algebra and basic statistics