<ul data-eligibleForWebStory="true"><li>AI models function as decision-making tools in AI systems, each with unique strengths and applications.</li><li>Common AI models include linear regression, used for numerical predictions like house prices.</li><li>Logistic regression is for classification tasks, such as spam detection or loan approval.</li><li>Decision trees operate as flowcharts to make decisions based on yes/no questions.</li><li>Random forests consist of multiple decision trees working together, each contributing to a final decision.</li><li>Support Vector Machines draw boundaries between data categories, useful for tasks like image classification.</li><li>K-Nearest Neighbors algorithm makes decisions based on proximity to other data points.</li><li>Naive Bayes relies on probability and assumes independence of features to classify items like emails.</li><li>K-Means Clustering is an unsupervised model that groups similar data points into clusters.</li><li>Neural Networks are inspired by the human brain and are used in advanced AI applications like image recognition.</li><li>Reinforcement learning models learn through trial and error, receiving rewards or penalties based on their actions.</li></ul>

10 Common AI Models Explained Simply: From Trees to Neural Networks

Discover more