Classification

Classification is a fundamental machine learning and statistical concept that involves categorizing data points into predefined classes or labels based on their features. It is a type of supervised learning where models are trained on labeled datasets to predict discrete outcomes, such as spam detection, image recognition, or medical diagnosis. Common algorithms include logistic regression, decision trees, support vector machines, and neural networks.

Also known as: Class prediction, Categorization, Labeling, Supervised classification, ML classification

Why learn Classification?

Developers should learn classification for building predictive models in applications like fraud detection, sentiment analysis, customer segmentation, and automated content moderation. It is essential in data science, AI, and analytics roles where pattern recognition and decision-making from structured or unstructured data are required, such as in finance, healthcare, and marketing industries.

Learning Resources

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