While machine learning is based on the idea that machines may learn and adapt via experience, AI refers to a broader concept in which machines can accomplish tasks "smartly." To solve real-world problems, artificial intelligence employs machine learning, deep learning, and other techniques.
Supervised model: A supervised model trains with labeled data which includes the desired outcome as well as the target value. This model can be trained to predict the outcome given a new input.
Unsupervised model: An unsupervised model does not need labeled data to train itself because it learns from unlabeled data. The outputs of an unsupervised model can be used by other machines to learn what the best possible outputs should be for any given input without needing any human intervention or labeling.
Text generation: Machine learning models can generate text that is similar to what a human would write without any human intervention.
Predictive analysis: Machine learning models can be used to predict customer behavior based on their previous interactions with companies or brands.
Customer analytics: Machine learning models can be trained on data such as clickstreams and purchase history to make predictions about customer behavior
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