Supervised machine learning is a type of machine learning that involves the use of labeled data to train a model. The model learns from the input data and its corresponding output labels to make predictions or decisions without human intervention. Here are some examples of supervised machine learning applications:

1. Spam Email Classification: Algorithms are trained on a dataset of emails that are labeled as “spam” or “not spam.” They then use this training to classify new incoming emails into one of these two categories.
2. Image Recognition: Supervised learning is used to classify images into different categories, such as identifying whether a given image contains a cat, dog, or another object. Training is done with labeled images.
3. Sentiment Analysis: Text data, like customer reviews, can be analyzed to determine the sentiment expressed in them. Models are trained on labeled data where texts are marked as positive, negative, or neutral.
4. Credit Scoring: By using historical financial data, models can be trained to predict a person’s creditworthiness. The data would include various features like income, previous loans, repayment history, etc., and labels would indicate whether the person defaulted on a loan.
5. Medical Diagnosis: Supervised learning can be used to predict diseases based on different diagnostic measures. For example, a model might be trained to predict whether a tumor is malignant or benign based on a series of medical images and laboratory results.
6. Stock Price Prediction: Models can be trained on historical stock prices and other financial indicators to predict future stock prices.
7. Handwriting Recognition: Supervised learning can be used to recognize handwritten characters or digits. Training data would include images of handwritten characters, and the corresponding labels would be the actual characters or numbers they represent.
8. Speech Recognition: Algorithms are trained on labeled datasets consisting of audio files and corresponding transcriptions to convert spoken language into written text.
9. Churn Prediction: In the business world, models can be trained to predict whether a customer is likely to leave a service or product based on their previous interactions and behavior.
10. Fraud Detection: Supervised learning can be used to detect fraudulent transactions in various sectors like banking or online retail. Models are trained on historical data where transactions are labeled as fraudulent or legitimate.

These examples represent a wide array of applications where supervised machine learning is used to derive insights, make predictions, and automate decisions across various industries and domains.