What are the main differences between supervised learning and unsupervised learning within the confines of machine learning? Explain your answer in 3-5 short small simple and clear sentences.
1. In supervised learning, the model is trained using labeled data, where the input data is paired with correct output labels. In unsupervised learning, the model is trained using unlabeled data without any specific target output.
2. Supervised learning helps the model to learn patterns and relationships between input features and output labels, making it suitable for tasks like classification and regression. Unsupervised learning, on the other hand, is used for tasks like clustering, dimensionality reduction, and anomaly detection where the focus is on exploring the underlying structure of the data.
3. Supervised learning requires a training dataset with labeled examples, while unsupervised learning can work with unlabelled data, making it more flexible and scalable for certain types of data analysis tasks.