Classification Algorithms

Classification algorithms are a subset of machine learning techniques used to predict categorical labels or classes for input data based on training examples. They are fundamental in supervised learning, where models learn patterns from labeled datasets to classify new, unseen instances into predefined categories. Common applications include spam detection, image recognition, and medical diagnosis.

Also known as: Classification Models, Classifiers, Supervised Classification, Categorical Prediction, ML Classification

Why learn Classification Algorithms?

Developers should learn classification algorithms when building predictive models for tasks involving discrete outcomes, such as fraud detection, customer segmentation, or sentiment analysis. They are essential in data science, AI, and analytics roles, enabling automated decision-making and pattern recognition in fields like finance, healthcare, and marketing.

Learning Resources

• docs: Scikit-learn Classification Documentation
• course: Coursera Machine Learning Course by Andrew Ng
• tutorial: Towards Data Science: Classification Algorithms Explained
• tutorial: Kaggle Classification Tutorial