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Supervised, Unsupervised & Reinforcement Learning

Enterprise Machine Learning

Summary

Organisations are exploring and generating real returns on their investments from Machine Learning, namely, Supervised Learning, Unsupervised Learning and Reinforcement Learning.

 

It’s a science that’s not new, but one that has gained fresh momentum due to unprecedented access to compute and storage on cloud platforms enabling organisations to quickly and automatically produce models that can analyse bigger, more complex data and deliver faster, more accurate results at scale.

 

Organisations are exposing underlying data patterns that were previously undiscovered, predicting behaviours and outcomes where there are many complex predictors, and training models to forecast scenarios before implementing expensive solutions, products and services.

 

These models also unlock and identify new opportunities and help avoid previously unknown risks.

ML Process & Requirements

Supervised Learning

The learning algorithm/model receives a labelled training data set, which is used to retrospectively identify predictors to create a predictive model. Some examples include:

Unsupervised Learning

The goal is to explore the data and find some structure (including clusters and associations) without receiving any training data. Some examples include:

Reinforcement Learning

The learning model discovers through trial and error which actions yield the greatest rewards. Some examples include:

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