Supervised learning is a type of machine learning where an algorithm learns from a labeled dataset. It is one of the most common types of machine learning problems, and it's widely used in various applications, such as image recognition, natural language processing, and speech recognition.
Key Concepts
- Labeled Data: This is data that is already classified or tagged with the correct output.
- Training: The process of teaching the algorithm to learn from the labeled data.
- Testing: The process of evaluating the algorithm's performance on new, unseen data.
Common Algorithms
- Linear Regression
- Logistic Regression
- Support Vector Machines (SVM)
- Decision Trees
- Random Forest
- Neural Networks
Real-World Applications
- Image Recognition: Identifying objects in images, such as identifying faces in a photo.
- Medical Diagnosis: Predicting diseases based on medical records.
- Financial Fraud Detection: Detecting fraudulent transactions in financial transactions.
- Recommender Systems: Suggesting products or content to users based on their preferences.
Further Reading
For more in-depth information on supervised learning, you can read our comprehensive guide on Machine Learning Algorithms.
Supervised Learning Example