AI Algorithms: Methods and Models for Machine Thinking

AI algorithms are methods and mathematical models that empower machines to think and learn, influencing technologies from search engines to self-driving cars. These algorithms enable machines to execute tasks that typically require human intelligence, such as language translation and speech recognition. There are three primary categories of AI algorithms: supervised learning, unsupervised learning, and reinforcement learning.

Supervised Learning

In supervised learning, algorithms learn from labeled datasets where the input data is associated with the correct output. This approach is applied to classification and regression problems, including linear regression, time-series regression, and logistic regression. Common examples of supervised learning algorithms include decision trees, support vector machines, and neural networks. Recommendation systems on streaming platforms and spam filters in email utilize supervised learning by learning from data to make predictions.

Key Supervised Learning Algorithms:

• Decision Tree: A chart that uses a branching shape representing possible outcomes, with each node representing a different categorization test.

• Random Forest: An algorithm that uses many decision trees, each testing for a different input, and makes a prediction based on the combined results.

• Linear Regression: A basic AI algorithm that makes predictions based on an independent variable. It can predict outcomes using historical data.

• Support Vector Machines (SVM): An algorithm used for classification or regression by plotting data on a chart and classifying the data points by finding the hyperplane that separates each class.

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