Maximum Likelihood Estimation for Data Science and Statistics

Statistical models are only as good as the parameters we choose for them, and maximum likelihood estimation (MLE) is the industry-standard method for finding those optimal parameters. This text-based course guides you from the fundamental mathematical concepts of probability and likelihood to the practical implementation of MLE in real-world data scenarios. You will understand how to formulate likelihood functions, work with log-likelihood for mathematical simplicity, and apply these concepts to solve estimation problems. What you'll learn: Understand the core mathematical theory behind probability density functions and likelihood estimation; Apply analytical methods to derive maximum likelihood estimators for common distributions; Configure statistical models using log-likelihood transformation techniques; Master the application of MLE in modern machine learning algorithms like logistic regression; Practice evaluating estimator properties including bias, consistency, and efficiency. The course begins with a clear introduction to foundational probability concepts and statistical notation before guiding you step-by-step through analytical derivations and practical computational applications. This course is designed for beginners in data science, statistics, and quantitative analysis who want to build a strong theoretical and practical foundation without complex prerequisites. Start mastering the mathematical backbone of data modeling today.