Supervised learning is a core concept in machine learning where algorithms learn from labeled data. This type of learning involves training a model on a dataset with input-output pairs to make predictions or decisions.
Key Concepts
- Labeled Data: Each training example includes input features and a corresponding correct output.
- Training Process: The model adjusts its parameters to minimize prediction errors.
- Common Tasks: Classification, regression, and forecasting.
Popular Algorithms
- Linear Regression
- Decision Trees
- Support Vector Machines (SVM)
Applications
- Predicting house prices (regression)
- Email spam detection (classification)
- Customer churn analysis (classification)
For deeper insights, explore our Machine Learning Course Catalog. 🚀