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Supervised vs. Unsupervised Machine Learning: Use Cases & Examples

Dataiku

One of the most fundamental concepts to master when getting up to speed with machine learning basics is supervised vs. unsupervised machine learning. This blog post provides a brief rundown, visuals, and a few examples of supervised and unsupervised machine learning to take your ML knowledge to the next level.

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Unsupervised Machine Learning: Use Cases & Examples

Dataiku

One of the most fundamental concepts to master when getting up to speed with machine learning basics is supervised vs. unsupervised learning. This blog post provides a brief rundown, visuals, and a few examples of unsupervised machine learning to take your ML knowledge to the next level.

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Fraud Detection using Deep Learning

Cloudera

One of the many areas where machine learning has made a large difference for enterprise business is in the ability to make accurate predictions in the realm of fraud detection. The research team at Cloudera Fast Forward have written a report on using deep learning for anomaly detection. a Hive Table).

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Data science vs. machine learning: What’s the difference?

IBM Big Data Hub

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is machine learning? This post will dive deeper into the nuances of each field. What is data science?

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Credit Card Fraud Detection using XGBoost, SMOTE, and threshold moving

Domino Data Lab

In this article, we’ll discuss the challenge organizations face around fraud detection, how machine learning can be used to identify and spot anomalies that the human eye might not catch. In contrast, the decision tree classifies observations based on attribute splits learned from the statistical properties of the training data.

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AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: What’s the difference?

IBM Big Data Hub

To keep up with the pace of consumer expectations, companies are relying more heavily on machine learning algorithms to make things easier. This blog post will clarify some of the ambiguity. How do artificial intelligence, machine learning, deep learning and neural networks relate to each other? What is machine learning?