Maximum Likelihood Estimation (MLE)

Maximum likelihood estimation (MLE) is a method of estimating some parameters in a probabilistic setting. It is based on finding the parameters of a probability distribution that maximise a likelihood function of the observed data. The idea is to find the probability density function under which the observed data is most probable, the most likely. This blog gives a brief MLE overview.

Linear Algebra with NumPy

Linear Algebra is essential to understand ML for three main reasons. One that when you read a book or an article of ML, models are very often explained with linear algebra. This is a consequence of much mathematical convenience as explained below. Second, many models are founded by linear algebra methods. Third, deep learning uses extensively vectors. In either way, if ML interest you, you need to trespass linear algebra. This article contains its most important notions with NumPy examples.

Continue reading “Linear Algebra with NumPy”

Create a website or blog at

Up ↑