Classification

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In this article let's explain the current and future applications of text classification. We are using text classification to make our business easier now. Classification of library books and segmentation of news articles is basically an example of text classification. By adding the same artificial intelligence technology, the process is automated, easier, and requires minimal manual work. The concept of using AI to classify text has been around for quite a while (automatic filtering and labeling in Gmail, ringing sound).

Text classification is an intelligent classification of text. In addition, automating these tasks using machine learning can only make the entire process ultra fast and efficient. Artificial intelligence and machine learning are the most useful techniques for recent progress. They are looking around the application. As Jeff Bezos mentioned in his annual shareholders meeting letter, especially regarding automatic text classification, we wrote about the technology behind it and its applications. The text classifier is being updated. In this article, we describe the technology, applications, customization, and segmentation related to the automatic text classification API.

The k-nearest neighbor (kNN) classification method is one of the simplest methods of machine learning and is an excellent method for introducing machine learning and classification. At the most basic level, it basically categorizes by finding the most similar data points in the training data and makes knowledgeable guesses based on that classification. Although this method is easy to understand and implement, it has a wide range of applications in various fields such as recommended system, semantic search, anomaly detection and so on.