Machine Learning Q&A

Machine learning is a subset of artificial intelligence that involves the use of algorithms and statistical models to enable computers to improve their performance on a specific task through experience or data.

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The main types are supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.

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Supervised learning is a type of machine learning where the model is trained on labeled data, meaning that each training example is paired with an output label.

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Unsupervised learning is a type of machine learning where the model is trained on unlabeled data and must find patterns and relationships within the data.

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Reinforcement learning is a type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize some notion of cumulative reward.

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A neural network is a series of algorithms that attempt to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.

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A decision tree is a model used for classification and regression that splits the data into subsets based on the value of input features, creating a tree-like structure of decisions.

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Overfitting occurs when a machine learning model performs well on the training data but poorly on new, unseen data because it has learned the noise and details of the training data too well.

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