What is supervised learning?

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Supervised and Unsupervised Learning are two primary types of machine learning, differing mainly in how they process and learn from data.

Neural networks are a type of machine learning model inspired by the structure and function of the human brain. They are designed to recognize patterns and relationships in data through a process of learning.

Overfitting is a common problem in machine learning where a model learns the training data too well, including its noise and outliers, resulting in excellent performance on the training set but poor generalization to new, unseen data.

Supervised learning is a type of machine learning where a model is trained on a labeled dataset. This means the input data comes with corresponding correct output labels, and the model learns to map inputs to the correct outputs.

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How Supervised Learning Works:

1. Training Data: You provide the algorithm with a dataset containing input-output pairs (e.g., images labeled with the object they contain).

2. Learning: The algorithm analyzes the data to learn a function or pattern that relates inputs to outputs.

3. Prediction: Once trained, the model can predict the output for new, unseen inputs.

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Examples of Supervised Learning:

• Classification: Assigning categories (e.g., email spam detection, recognizing handwritten digits).

• Regression: Predicting continuous values (e.g., forecasting house prices, predicting temperature).

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