Parameter Estimation in Machine Learning: MLE, MAP, and Bayesian Inference

Behind every successful machine learning model lies the fundamental challenge of finding the right parameters to fit your data. Understanding how algorithms actually find these values is essential for building robust, predictable, and reliable systems. This text-based course guides you through the core conceptual and mathematical frameworks of parameter estimation, helping you transition from treating machine learning models as black boxes to deeply understanding the probabilistic foundations that drive their training processes. What you'll learn: - Understand foundational concepts of probability, likelihood, and prior distributions - Calculate Maximum Likelihood Estimation (MLE) for common data distributions - Apply Maximum A Posteriori (MAP) estimation to incorporate prior knowledge into your models - Implement basic Bayesian inference to estimate full parameter distributions rather than single points - Write clean Python code using modern scientific libraries to solve practical estimation problems - Compare the trade-offs between frequentist and Bayesian approaches in real-world scenarios You will start with key terminology and the essential mathematical definitions of probability, joint distributions, and likelihood. From there, you will progress through step-by-step written explanations, working through practical calculations and clear code-based examples that illustrate how these techniques apply to modern machine learning workflows. This course is designed for beginner data scientists, software engineers, and analysts who want to understand the math behind machine learning models. No prior background in advanced Bayesian statistics is required, though basic familiarity with algebra and Python is helpful. Start reading today to demystify how machine learning models learn from data.