There are many algorithms used in AI coding, each with its own strengths and weaknesses. Here are some common algorithms used in AI :

Linear Regression : A supervised learning algorithm used to model the relationship between dependent and independent variables.

Logistic Regression : A supervised learning algorithm used for classification problems.

Decision Trees : A supervised learning algorithm that creates a tree-like model of decisions and their possible consequences.

Random Forest : An ensemble learning algorithm that combines multiple decision trees to improve accuracy and reduce overfitting.

Support Vector Machines (SVM) : A supervised learning algorithm used for classification and regression problems. k-Nearest Neighbors (k-NN) : A supervised learning algorithm used for classification and regression problems.

Naive Bayes : A probabilistic algorithm used for classification problems.

Artificial Neural Networks : A family of algorithms inspired by the structure and function of the human brain, used for a variety of problems including classification, regression, and image and speech recognition.

Convolutional Neural Networks (CNNs) : A type of neural network specifically designed for image recognition.

Recurrent Neural Networks (RNNs) : A type of neural network specifically designed for sequential data analysis, such as natural language processing.

These are just a few examples of the algorithms used in AI coding. The choice of algorithm will depend on the problem being solved and the type of data being used. It is important to choose the appropriate algorithm for the problem to achieve the best results.