This blog post provides a comprehensive guide to understanding normal distributions, often referred to as Gaussian distributions. It covers foundational concepts such as discrete and continuous variables, probability distribution, expectation, variance, standard deviation, the normal distribution equation, the 68-95-99 rule, and z-scores. The post employs practical examples and Python code to illustrate these mathematical concepts, pertinent to both engineering and machine learning contexts.

pmf

Probability, a high-school math staple, often gathers rust in our memories. In this blog, we refresh its concepts through a machine learning lens, delving into Probability Mass Function (PMF). By the blog’s end, readers gain insights into probability, distribution, PMF’s expectations, and variance—crucial aspects in machine learning. The code snippet illustrates PMF for a biased coin toss, emphasizing its role in predicting outcomes. Bridging probability theory and machine learning, the blog fosters a deeper understanding of these essential concepts.