Maximum Likelihood Estimation Example - Fitting a Normal Distribution with Data

Learn how to apply maximum likelihood estimation by working through a complete example of fitting a normal distribution to data in this 16-minute tutorial. Begin by formulating the joint probability density function for multiple data points, then progress through calculating the log joint PDF and log likelihood function. Derive the optimization conditions by taking derivatives and setting them to zero, followed by step-by-step solutions for estimating both the mean (mu) and standard deviation (sigma) parameters. Master the mathematical foundations of parameter estimation through clear derivations and practical implementation of this fundamental statistical method used across machine learning and data science applications.