Machine Learning is something we come across every day, whether it’s the referral services by Amazon or Netflix, face recognition by Facebook or email applications to identify spam or advertising.
But what is Machine Learning exactly and how do systems learn and evolve by themselves?
WHAT IS MACHINE LEARNING?
Machine Learning describes the acquisition of knowledge by an artificial system. The computer generates knowledge from experience in the same way as a human being does and can independently find solutions for new and unknown problems. A system analyzes examples and uses self-learning algorithms to identify certain patterns and laws in the data. The aim of machine learning is to intelligently link data, identify correlations, draw conclusions and ultimately make predictions.
ELEMENTS OF MACHINE LEARNING
Machine Learning systems usually consist of three main components:
Model: a system making predictions and identifications
Parameter: signals or factors the system uses to make decisions
Learning system: a system that customizes parameters and the model by comparing the predictions to the actual result
CREATING THE MODEL
The model often starts with a forecast about a certain situation which the Machine Learning system then uses to evolve. The model itself depends on the parameters deployed for the calculations. The system expresses the forecast with a mathematical equation and provides a trend line of what is expected.
INPUT OF EXISTING ACTUAL DATA
Once the model has been established, it takes real information to feed the system. The system analyzes the data to recognize trends. The results will most likely deviate from the previously created forecast and trend line.
THE SYSTEM LEARNS
The entered data is continuously checked by the system and used to learn and improve the model. With mathematical algorithms it customizes the initial assumptions to come up with a new forecast that is closer to the actual trend.
Most importantly, you need to understand that the system adapts the parameters to gradually refine the model consistently.
The system is constantly fed with new data, which again is compared to the revised model. If the model was successful, the results are now closer to the forecast.
However, it’s a never-ending process. The parameters are continuously adapted by the system to refine and re-create the model. There is another comparison with a new set of data entered in the system, which customizes the model even further.
In an ever-repeating cycle, the model becomes more precise with every new set of data to make forecasts that are even more accurate. The system keeps adapting in order to evolve even further and to learn from the existing data.
MACHINE LEARNING IN RELATION TO ARTIFICIAL INTELLIGENCE
Many use the terms Machine Learning and Artificial Intelligence synonymously. Machine Learning however is only a part of AI.
Artificial Intelligence generally deals with the automation of human intelligent behavior. Besides Machine Learning, pattern recognition, robotics, machine translation and processing natural language are all part of AI. Presently, however, Machine Learning is considered one of the core and most successful disciplines of AI.
Find out more in our post “Artificial Intelligence in Marketing: Four areas that had major influence on AI”.