TL;DR: Supervised Learning

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Thanks to Chatty for this fast overview of supervised learning. Our complete index of articles chronicles the rapidly emerging technologies fueling the artificial intelligence revolution.

Supervised learning is a type of machine learning where an algorithm is trained on a labeled dataset, meaning that the input data is paired with corresponding output labels. The goal of supervised learning is to learn a mapping or relationship between the input features and the target labels so that the algorithm can make predictions on new, unseen data.

In supervised learning:

1. Labeled Dataset:The training dataset consists of input-output pairs, where each input is associated with a known output or label.The labeled dataset serves as the basis for the algorithm to learn patterns and relationships.
2. Training Process:During the training phase, the algorithm uses the labeled data to adjust its internal parameters or weights.The objective is to minimize the difference between the predicted outputs and the true labels.
3. Prediction:Once the model is trained, it can make predictions on new, unseen data.The algorithm generalizes its learning to make predictions for input data it has not encountered before.
4. Evaluation:The performance of the model is evaluated on a separate dataset, called the testing or validation set, to assess how well it generalizes to new data.
5. Common Algorithms:Linear Regression: Used for predicting a continuous target variable.Support Vector Machines (SVM): Effective for classification and regression tasks.Neural Networks: Deep learning models with multiple layers, suitable for complex tasks.
6. Applications:Supervised learning is widely used in various applications, including image recognition, speech recognition, natural language processing, and recommendation systems.

Examples of supervised learning tasks include:

• Classification: Assigning input data to predefined categories (e.g., spam or not spam).
• Regression: Predicting a continuous numeric value (e.g., predicting house prices).

Supervised learning is powerful when there is a clear relationship between inputs and outputs, and when labeled training data is available for the algorithm to learn from.

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