This is the last module on Foundational Machine Learning before we dive into our project and implement our Machine Learning models. This section is also optional since its not covered much in interviews.
In this article, we assume why most assumptions about our data revolve assuming that it is Normally Distributed. In essence, normal distribution is the least committal we can get about the distribution of our data points given that we are only able to estimate its mean and variance. This forms the foundation of most of the mathematical nuances that we’ll see in the coming machine learning modules and should be a fun read for those following along!