By scipio on Skatehive
Learn AI Series (#35) - Data Ethics and Bias in ML What will I learn You will learn how bias enters ML systems -- from data collection through deployment; the major types of bias: selection, measurement, confirmation, survivorship, historical; fairness metrics and why "fair" doesn't have a single mathematical definition; practical debiasing techniques: resampling, reweighting, threshold adjustment; differential privacy concepts -- protecting individual data in trained models; building fairness auditing tools from scratch so you can measure what matters; when NOT to use ML -- because sometimes a simple rule is both safer and fairer. Requirements A working modern computer running macOS, Windows or Ubuntu; An installed Python 3(.11+) distribution; The ambition to learn AI and machine learning. Difficulty Beginner Curriculum (of the Learn AI Series): Learn AI Series (#1) - What Machine Learning Actually Is Learn AI Series (#2) - Setting Up Your AI Workbench - Python and NumPy Learn AI Seri