Day 42 of #60daysOfMachineLearning
🔷 Supervised Learning 🔷
Supervised learning is a type of machine learning algorithm that uses labeled training data to learn a function that can map input data to the desired output.
🔷 Supervised Learning 🔷
Supervised learning is a type of machine learning algorithm that uses labeled training data to learn a function that can map input data to the desired output.
In supervised learning, the training data consists of a set of input data and corresponding labels or output values, which are used to train the model to produce the desired output for a given input.
For example, in a supervised learning model for image classification, the training data would consist of a set of images and their corresponding labels, such as "dog", "cat", or "car".
The model would learn a function that can take an input image and predict its label based on the training data.
Supervised learning algorithms can be used for a wide range of tasks, including classification, regression, and prediction.
Some common supervised learning algorithms include decision trees, support vector machines, and neural networks.
Overall, supervised learning is a powerful and widely used technique in machine learning that allows models to learn from labeled data and make predictions or decisions based on that learning.
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