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Fraud Detection  Content on InfoQ rss

Presentations about Fraud Detection rss

AI, ML & Data Engineering Follow 1005 Followers ML Data Pipelines for Real-Time Fraud Prevention @PayPal by Mikhail Kourjanski Follow 1 Followers Posted on Aug 22, 2018
AI, ML & Data Engineering Follow 1005 Followers Real-Time Data Analysis and ML for Fraud Prevention by Mikhail Kourjanski Follow 1 Followers Posted on May 31, 2018
AI, ML & Data Engineering Follow 1005 Followers Data Pipelines for Real-Time Fraud Prevention at Scale by Mikhail Kourjanski Follow 1 Followers Posted on May 23, 2018
AI, ML & Data Engineering Follow 1005 Followers Counterfactual Evaluation of Machine Learning Models by Michael Manapat Follow 2 Followers Posted on May 10, 2018
AI, ML & Data Engineering Follow 1005 Followers AI in Finance: from Hype to Marketing and Cybersec Applications by Natalino Busa Follow 0 Followers Posted on Nov 16, 2017
AI, ML & Data Engineering Follow 1005 Followers Fighting Online Fraud and Abuse with Large-Scale Machine Learning at Sift Science by Jacob Burnim Follow 0 Followers Posted on Nov 06, 2017
AI, ML & Data Engineering Follow 1005 Followers Solving Payment Fraud and User Security with ML by Soups Ranjan Follow 0 Followers Posted on Oct 05, 2017
AI, ML & Data Engineering Follow 1005 Followers Large Scale Machine Learning for Payment Fraud Prevention by Venkatesh Ramanathan Follow 0 Followers Posted on Aug 05, 2017

News about Fraud Detection rss

AI, ML & Data Engineering Follow 1005 Followers Fighting Financial Fraud with Machine Learning at Airbnb by Srini Penchikala Follow 38 Followers Posted on Mar 20, 2018

Articles about Fraud Detection rss

AI, ML & Data Engineering Follow 1005 Followers Article Series: An Introduction to Machine Learning by Michael Manapat Follow 2 Followers Posted on Feb 27, 2017 In this series, we give an introduction to some powerful but generally applicable techniques in machine learning. These include deep learning but also more traditional methods that are often all the modern business needs. After reading the articles in the series, you should have the knowledge necessary to embark on concrete machine learning experiments in a variety of areas on your own.

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