Machine learning (ML) is a subfield of artificial intelligence that gives computers the ability to learn from data without being explicitly programmed for every possible scenario. Instead of following a rigid set of rules, ML systems use algorithms to find patterns and make predictions. The more data they are given, the better they get at recognizing these patterns and improving their performance

How Machine Learning Works

The process of machine learning generally involves several steps:

1. Data Collection and Preparation: ML models need large amounts of high-quality data to learn from. This data is collected, cleaned, and organized to remove errors and prepare it for analysis.

2. Model Training: An algorithm is selected and trained on the prepared data. The algorithm iteratively analyzes the data, adjusting its internal parameters to minimize the difference between its predictions and the actual outcomes.

3. Model Evaluation: The trained model is tested on a new, separate dataset to see how well it performs on unseen data. Performance metrics like accuracy are used to assess its effectiveness.

4. Deployment and Monitoring: Once a model performs well, it can be deployed for real-world use. It continues to be monitored to ensure its accuracy and to update it with new data over time.