Supervised Machine Learning is an algorithm using labeled training data to enable the prediction of unlabeled data. This technique, combined with unsupervised and reinforcement learning, makes up three of the major paradigms of machine learning. It is analogous to an instructor or supervisor guiding the learning process, as it involves information already marked with expected results. Supervised learning functions by providing a dataset containing correct and incorrect outputs, allowing models to improve over repeated attempts. Loss functions can be used to measure accuracy and adjusted to minimize error rates.
How is Supervised Learning Applied Across Various Domains?
Supervised learning algorithms can be used to
- Classify images based on their content. It is also used in facial recognition, object detection, and other computer vision applications.
- Recognize spoken words and phrases used in virtual assistants like Siri and Alexa.
- Classify text according to content used in sentiment analysis, spam detection, and other NLP applications.
- Diagnose medical conditions based on patient data, medical history, symptoms, and lab results.
- Identify fraudulent transactions in the banking and finance sector.
- Predict and use customer behavior for customer segmentation, product recommendations, and other marketing applications.
- Predict weather patterns based on historical data and live conditions.
- Train self-driving cars to recognize and respond to a variety of driving scenarios.
- Find the shortest route to a destination quickly and accurately and predict traffic conditions based on real-time location data.
- Automate filtering techniques to ensure important emails are delivered to the inbox while potential spam is diverted away.
