Uplift Modeling

By Alejandro Correa and Maria Fernanda Cortes  This post is part of a series in which I’m discussing several parts of my AI_at_Rappi presentation. In a previous post I discussed a particular algorithm for recommending restaurants called rest2vec, In a follow-up, I discussed how to include financial costs when analyzing a churn model.  This time... Continue Reading →

Hunting Malicious Certificates with Deep Learning

Seeing the signature green padlock and “https” in the browser bar means one thing for most internet users: safety. However, is this sense of security justified?  The short answer is a loud, resounding, no! To start, let’s define what “https” really means: that the website being accessed is encrypted, and all information sent through the site is protected by... Continue Reading →

Machine Learning Algorithms – Naive Bayes Classifier

A Naive Bayes Classifier is a supervised machine-learning algorithm that uses the Bayes’ Theorem, which assumes that features are statistically independent. The theorem relies on the naive assumption that input variables are independent of each other, i.e. there is no way to know anything about other variables when given an additional variable. Regardless of this assumption, it... Continue Reading →

Machine Learning Algorithms – K-Means Clustering

  Previously in this series, we have looked at decision trees and random forests, two types of supervised learning algorithms. Supervised algorithms are trained with data that provides input vectors as well as their corresponding target vectors, or the output that is expected after the data is processed. Unsupervised models, on the other hand, are trained using data... Continue Reading →

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