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 →

Building AI Applications Using Deep Learning

Recently, we have seen a huge boom around the field of deep learning; it is currently being implemented in a wide variety of fields, from driverless cars to product recommendation. In their most primitive form, deep learning algorithms originated in the 1960s. If the concept has been around for decades, why is it that widespread... Continue Reading →

Machine Learning Explained

Machine learning models are often dismissed on the grounds of lack of interpretability. There is a popular story about modern algorithms that goes as follows: Simple linear statistical models such as logistic regression yield to interpretable models. On the other hand, advanced models such as random forest or deep neural networks are black boxes, meaning... Continue Reading →

TDWI: 5 Minutes with a Data Scientist: Alejandro Correa Bahnsen of Easy Solutions Lead data scientist Alejandro Correa Bahnsen develops machine learning algorithms for fraud detection. He described for Upside the basic skills and personality traits he believes are necessary to succeed in data science. [Read More]

Applying Data Science to Fraud Prevention

Eighty thousand Kindle users. Sixty-five million Tumblr users. What do they have in common? Both groups had their login credentials breached, courtesy of hackers. While these attacks didn’t directly target financial accounts,the information contained in these breaches is likely being sold on the Dark Web and being used to build a larger profile that will... Continue Reading →

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