Overview
Supervised learning is a type of machine learning where we teach a model using labeled data. This means we provide the model with input data and the correct output. The model learns to make predictions based on this information. Supervised learning is widely used in applications like email filtering, speech recognition, and image classification. Understanding this concept is essential for anyone looking to work in data science or artificial intelligence.
📚 Key Learning Objectives
- ✓ Define supervised learning and its importance.
- ✓ Identify examples of supervised learning applications.
- ✓ Differentiate between classification and regression tasks.
- ✓ Explain the role of labeled data in supervised learning.
- ✓ Evaluate the performance of a supervised learning model.
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