Supervised Learning

Supervised learning is a machine learning paradigm where an algorithm learns from labeled training data to make predictions or decisions. It involves mapping input data to known output labels, with the goal of generalizing this mapping to new, unseen data. Common tasks include classification (predicting discrete categories) and regression (predicting continuous values).

Also known as: Supervised ML, Supervised Machine Learning, Labeled Learning, Predictive Modeling, SL

Why learn Supervised Learning?

Developers should learn supervised learning when building predictive models for applications like spam detection, image recognition, or sales forecasting, as it leverages labeled data to achieve high accuracy. It is essential in fields such as healthcare for disease diagnosis, finance for credit scoring, and natural language processing for sentiment analysis, where historical data with clear outcomes is available.

Learning Resources

• docs: Supervised Learning - Scikit-learn Documentation
• course: Machine Learning by Andrew Ng (Coursera)
• tutorial: Supervised Learning Tutorial - TensorFlow
• book: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
• video: Supervised Learning Explained - StatQuest