This module introduces the end-to-end machine learning pipeline, from raw data to ML models. It covers:
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Data preparation and preprocessing: Cleaning, transforming, and handling missing or noisy data.
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Feature extraction and selection: Identifying the most relevant features to improve model performance.
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Supervised learning: Regression (predicting continuous values) and classification (predicting discrete categories).
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Unsupervised learning and clustering: Discovering hidden patterns and grouping similar data points.
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Model evaluation and deployment: Assessing performance, avoiding overfitting, and applying models to real-world problems.
Students gain hands-on experience implementing ML pipelines using Python and popular ML libraries, preparing them to solve practical problems.
- Teacher: Asma Mansour