Supervised Learning: A Fundamental Machine Learning Concept
Supervised learning is a core area of machine learning where algorithms learn from labeled training data. Unlike unsupervised learning, it requires input-output pairs to train models, making it ideal for tasks like classification and regression.
Key Characteristics
- Labeled Data: Each training example includes a target variable (e.g.,
y = f(x)). - Predictive Modeling: The goal is to predict future outputs based on learned patterns.
- Feedback Mechanism: Models are evaluated on test data to refine accuracy.
Common Algorithms
- Linear Regression 📈
- Decision Trees 🌳
- Support Vector Machines (SVM) 📊
- Neural Networks 🧠
Applications
- Predicting house prices (regression)
- Classifying emails as spam or not (classification)
- Recognizing handwritten digits (e.g., MNIST dataset)
For a deeper dive into related topics, check our tutorial on Linear Regression. 🚀