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AI needs to provide experience, in other words, it needs data. The more data that enters the AI system, the more accurately the computer interacts with it and with the information it receives in the future. The higher the accuracy of the interaction, the more successful the task will be and the higher the degree of predictive accuracy.
What Types of ML Exist?
There are several types of ML. To date, the most popular are:
- Supervised learning
- Unsupervised learning
- Semi-supervised learning
In the context of classification, the learning algorithm can, for example, provide a history of credit card transactions, each of which is marked as either safe or suspicious. It should study the relationship between these two classifications to then be able to label new operations accordingly depending on their classification parameters (for example, the place of purchase, the time between operations, etc.).
What is Supervised Learning?
Methods for unsupervised learning issues try to identify similarities in the input data and divide them into categories.
Supervised learning is teaching a device to look for patterns by its example. As a rule, the engineer controls the entire learning process of the algorithm.
Throughout the process, the system is given massive arrays of marked-up information, for instance, pictures of different fruit with annotations pointing to bananas and apples. It will learn to identify clusters of pixels and forms connected with each object with enough examples. Consequently, it will be capable of accurately recognizing them in photographs.
