Machine Learning - Extract Systems

What is Machine Learning?

Machine learning is a type of artificial intelligence that automates analytical models to continuously improve upon themselves. It is based on the idea that systems can identify patterns, learn from data, and make decisions with minimal human input. The goal is to try to make computers “think” and act the same way humans do without relying on explicit pattern recognition and algorithms.

Machine learning systems are made up of 3 major parts:

                                        
Model
the system making predictions or identifications

                                        
Parameters
the criteria used by the model to formulate its decisions

                                        
Learner
the system that adjusts the model by looking at the differences in actual outcomes vs. predictions

There are four approaches to machine learning, they are broken into categories based on how the machine learns.

Supervised Learning – teaches model by example, with human input informing model changes
Unsupervised Learning – finding identifiable patterns in data and results to spot similarities and outliers to allow a model to self correct
Semi-Supervised Learning – a small amount of human supervision to label small, specific pieces of data to empower a model to produce better results
Reinforcement Learning – a trial and error process in which the proper actions are emphasized

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