Explain the concept of supervised learning in ML and provide examples of algorithms used in this approach.

Supervised learning is a popular approach in machine learning (ML) where the algorithm learns from labeled training data to make predictions or take actions. In supervised learning, the input data is paired with corresponding output labels, and the algorithm learns the relationship between the inputs and outputs. The process of supervised learning involves the following steps: 1. Data Collection: A labeled dataset is prepared where each input data point is associated with its corresponding output label. For example, in a spam email classification task, the input data would be emails, and the output labels would be whether the email is spam or not. 2. Training Phase: The algorithm is trained on the labeled dataset to learn the mapping between the input data and output labels. During training, the algorithm adjusts its internal parameters based on the input-output pairs to minimize the prediction errors. 3. Prediction Phase: Once the model is trained, it can make pr....