Classification Tasks

Classification tasks are a fundamental type of supervised machine learning problem where the goal is to predict a categorical label or class for a given input data point. They involve training a model on labeled data to learn patterns that distinguish between different classes, such as spam vs. not-spam emails or image categories like cats vs. dogs. This concept is widely applied in areas like natural language processing, computer vision, and medical diagnosis.

Also known as: Classification, Class Prediction, Categorization, Supervised Classification, ML Classification

Why learn Classification Tasks?

Developers should learn about classification tasks when building applications that require automated decision-making based on data, such as sentiment analysis in social media, fraud detection in finance, or disease prediction in healthcare. It is essential for implementing AI features that categorize inputs, enabling systems to handle tasks like email filtering, customer segmentation, or object recognition in images.

Learning Resources

• docs: Scikit-learn Documentation: Classification
• course: Coursera: Machine Learning by Andrew Ng
• tutorial: Towards Data Science: Classification in Machine Learning
• tutorial: Kaggle: Titanic - Machine Learning from Disaster