Supervised learning is a type of machine learning where the algorithm learns from labeled training data. This tutorial will provide an overview of supervised learning, including common algorithms and their applications.
Common Supervised Learning Algorithms
- Linear Regression: Used for predicting continuous values.
- Logistic Regression: Used for binary classification.
- Support Vector Machines (SVM): Effective in high-dimensional spaces.
- Decision Trees: Easy to interpret and visualize.
- Random Forest: An ensemble method that combines multiple decision trees.
- Neural Networks: Used for complex patterns and non-linear relationships.
Applications of Supervised Learning
- Image Recognition: Identify objects in images.
- Speech Recognition: Convert spoken words into text.
- Medical Diagnosis: Predict diseases based on patient data.
- Financial Fraud Detection: Identify fraudulent transactions.
Further Reading
For more detailed information on supervised learning, you can read our comprehensive guide on Machine Learning.