Machine Learning Fundamentals and Algorithmic Bias

As machine learning systems increasingly influence critical decisions in hiring, finance, and healthcare, understanding how these models work—and where they fail—is more essential than ever. This text-only course introduces you to the core principles of artificial intelligence while shining a spotlight on the critical issue of algorithmic bias. Through structured written explanations and conceptual exercises, you will transition from a curious beginner to a conscientious analyst capable of recognizing bias in data and algorithms. You will learn how machine learning models are trained, how historical biases creep into datasets, and how to apply modern fairness frameworks to evaluate automated systems. What you'll learn: - Understand the foundational concepts of machine learning, training data, and predictive modeling. - Identify common sources of algorithmic bias, from historical inequalities to representation gaps. - Measure model fairness using modern metrics such as demographic parity and equalized odds. - Analyze real-world case studies where biased algorithms caused unintended social consequences. - Evaluate mitigation strategies to design more equitable and transparent machine learning workflows. - Recognize emerging bias challenges in modern generative AI and large language models. The course begins by breaking down key terminology and foundational data concepts before moving into practical frameworks for detecting and addressing bias. You will explore these complex ethical and technical issues entirely through clear, written explanations and thought-provoking analysis exercises. This course is designed for absolute beginners, including aspiring data professionals, policy advocates, and product managers, with no prior programming or advanced mathematical background required. Start reading today to build a fairer, more ethical future for artificial intelligence.