Machine Learning

Machine learning is a subfield of artificial intelligence that focuses on giving machines the ability to learn from data, without being explicitly programmed. It is based on the idea that machines can learn from data, identify patterns and make decisions with minimal human intervention. Machine learning algorithms can be categorized into three groups: supervised learning, unsupervised learning, and reinforcement learning.

Supervised Learning

Supervised learning algorithms learn from labeled data. The algorithm is presented with input-output pairs, and it learns a mapping from the input to the output. For example, a supervised learning algorithm can be trained to recognize handwritten digits by being presented with labeled images of digits. The algorithm learns to map each image to its respective digit label.

Unsupervised Learning

Unsupervised learning algorithms learn from unlabeled data. The algorithm is presented with input data, and it tries to identify patterns or structure in the data. For example, an unsupervised learning algorithm can be used to cluster customers based on their purchasing behavior. The algorithm learns to group together customers with similar purchasing behavior.

Reinforcement Learning

Reinforcement learning algorithms learn from feedback. The algorithm is presented with an environment, and it learns to take actions that maximize a reward signal. For example, a reinforcement learning algorithm can be used to teach a robot to play a game. The algorithm learns to take actions that maximize the score.

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