Logistic Regression with Maximum Likelihood

Logistic regression is a statistical model that predicts the probability that a random variable belongs to a certain category or class. In this video we use the Sigmoid function to form our hypothesis (statistical model). After that we form our likelihood function as a Bernoulli distribution given a data set, and using the maximum likelihood estimation method the model parameters are estimated using the gradient ascent algorithm. ** SUBSCRIBE: https://www.youtube.com/c/EndlessEngineering?sub_confirmation=1 ** Follow us on Instagram for more endless engineering: https://www.instagram.com/endlesseng/ ** Like us on Facebook: https://www.facebook.com/endlesseng/ ** Check us out on twitter: https://twitter.com/endlesseng ** Cat photo is courtesy of Dan Perry on Flicker and is licensed under creative commons as Attribution 2.0 Generic (CC BY 2.0). Source: https://www.flickr.com/photos/golf_pictures/2187242989 License: https://creativecommons.org/licenses/by/2.0/ ** Dog photo: Available in the public domain at Pxhere. source: https://pxhere.com/en/photo/1455575