Artificial neural networks (ANNs)

A type of machine learning model that is inspired by the structure of the human brain. ANNs are capable of learning complex patterns from data and can be used for a wide range of tasks, including classification, regression, and natural language processing.

Decision trees

A supervised learning algorithm that uses a tree-like structure to make predictions. Decision trees are easy to interpret and can be used for both classification and regression tasks.

Gradient boosting

A supervised learning algorithm that combines multiple weak learners to produce a strong learner. Gradient boosting algorithms are often used for classification and regression tasks.

K-means clustering

An unsupervised learning algorithm that groups data points into K clusters based on their similarity. K-means clustering is a common algorithm for customer segmentation and image segmentation.

K-nearest neighbors (KNN)

A supervised learning algorithm that predicts the value of a new data point by finding the K most similar data points in the training set. KNN is a simple and versatile algorithm, but it can be computationally expensive for large datasets.

Linear regression

A supervised learning algorithm used to predict continuous values, such as house prices or customer churn.

Logistic regression

A supervised learning algorithm used to predict binary values, such as whether or not a customer will click on an ad.

Naive Bayes

A supervised learning algorithm that is based on Bayes' theorem. Naive Bayes models are simple and efficient, but they can be less accurate than other models on complex problems.

Random forest

A supervised learning algorithm that combines multiple decision trees to make predictions. Random forests are more accurate than individual decision trees and are resistant to overfitting.

Support vector machines (SVMs)

A supervised learning algorithm that uses hyperplanes to separate data points into different classes. SVMs are well-suited for problems with high-dimensional data.