Supervised Learning
Supervised learning relies on labeled data—input-output pairs where the "correct answer" is provided (e.g., images tagged as "cat" or "dog"). The algorithm's goal is to learn a mapping function from inputs to outputs, adjusting its internal parameters to minimize errors.
Example: Think of teaching a child with flashcards. You show a picture (input) and say the object's name (output). Over time, the child generalizes—recognizing new cat pictures even if they differ from the training examples. Example: Email filters learn from thousands of labeled "spam" and "not spam" emails to classify future messages.
Classification is a fundamental task in machine learning where we train models to categorize data into predefined classes or categories. Algorithms learn patterns from labeled examples to make predictions on new, unseen data.
Example: Classification is like sorting emails into folders such as "important," "promotions," or "spam." Decisions are based on features like sender, subject, and content. Problems include binary, multi-class, and multi-label classification. Various algorithms tackle classification differently, using techniques like logistic regression, SVMs, decision trees, and neural networks. Models are evaluated using metrics such as accuracy, precision, recall, F1-score, and ROC curve area. Real-world applications include email filtering, sentiment analysis, medical diagnosis, face recognition, and fraud detection.
Regression is a statistical technique that models relationships between input variables and continuous outcomes. Unlike classification, regression predicts numeric values, which is essential for forecasting and trend analysis.
Example: Think of regression as drawing a line of best fit through scattered data points. For example, a housing price model might show that each extra square foot adds about $150 to the price. Methods range from simple linear regression to non-linear models like polynomial regression. These techniques form the foundation for predictive systems in finance, healthcare, and environmental science.