Artificial intelligence models use advanced algorithms, lots of data, and smart machine learning to make predictions. These models look like humans and can help us solve problems and make better decisions.

Types of Artificial Intelligence models:

>Multiple types of AI models exist; they each have strengths and weaknesses. The four main types of AI models        are:

 

Rule-based models

>Rule-based models are designed to follow rules or logic to make decisions. These models are often used in            expert systems and decision support systems, where the rules are based on expert knowledge in a particular          domain.

 

Supervised learning models

>Supervised learning models are trained on data that has a label for each input value. Models like parallelized          and flexible convolutional neural networks are used for image recognition, natural language processing, and          fraud detection in finance, insurance, and healthcare. These models work well with complex data sets. They're        going to become more common as artificial intelligence develops.

 

Unsupervised learning models

>Unsupervised learning models are trained using data that has no associated label. These models are used in          tasks such as clustering and anomaly detection.

 

Reinforcement learning models

>Reinforcement learning models learn by making mistakes and getting feedback from rewards or penalties. These    models are used in tasks such as gaming and robotics.