Machine Learning model is the end result that happens after an ML algorithm processes the sample data it was fed during the training phase. When the algorithm is in production, the ML model analyzes text to derive information or make predictions, as in the case of Natural Language Processing.
Applications of Model in Artificial Intelligence
1. Predictions
Models use historical data to make predictions about trends or results. For instance, an ML model can predict whether the company will face customer churn or how its stock price will be in the future.
2. Classifications
Models can categorize data based on their features, such as the classification of images into different types of categorizing emails into received, sent, or spam.
3. Clustering
Models cluster similarly-featured data points into groups. A classic example of clustering is the segmentation of customers based on their persona, buying behavior, and purchase history.
4. Recommendation
In the retail industry, models recommend products or services to customers based on their shopping preferences and past purchases. For example, models will recommend the latest books or movies if a customer has a good history of reading books or watching movies. These recommendations are meant to simplify and expedite the customer’s purchase process.
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