Reinforcement Learning
Reinforcement Learning is a type of machine learning algorithms where an agent learns to make successive decisions by interacting with its surroundings. The agent receives the feedback in the form of incentives or punishments based on its actions. The agent’s purpose is to discover optimal tactics that maximize cumulative rewards over time through trial and error. Reinforcement learning is frequently employed in scenarios in which the agent must learn how to navigate an environment, play games, manage robots, or make judgments in uncertain situations.
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Reinforcement Learning |
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Machine Learning Algorithms
Machine learning algorithms are computational models that allow computers to understand patterns and forecast or make judgments based on data without the need for explicit programming. These algorithms form the foundation of modern artificial intelligence and are used in a wide range of applications, including image and speech recognition, natural language processing, recommendation systems, fraud detection, autonomous cars etc.
This Machine learning Algorithms article will cover all the essential algorithms of machine learning like Support vector machine, decision-making, logistics regression, naive bayees classifier, random forest, k-mean clustering, reinforcement learning, vector, hierarchical clustering, xgboost, adaboost, logistics, etc.