👉 Maximum Likelihood Estimation (MLE) is a statistical method used to estimate the parameters of a probability distribution based on observed data. In the context of ML, we assume that the data follows a specific model, such as a Gaussian distribution, and our goal is to find the parameters (like mean and variance) that maximize the likelihood of observing the given data. The likelihood function measures how probable the observed data is given the parameters, and MLE finds the parameter values that make this probability as high as possible. This approach is widely used in machine learning and data fitting, providing a principled way to infer underlying patterns from data by optimizing the model's fit.
