Machine Learning (ML) is a subset of artificial intelligence (AI) that focuses on the development of algorithms and statistical models that enable computer systems to perform tasks without being explicitly programmed. The central idea behind machine learning is to allow computers to learn and improve their performance on a specific task by analyzing and adapting to data.

1. Model Evaluation
• After training, the model is evaluated using a separate set of data not used during the training phase (test data). Evaluation metrics help assess the model's accuracy, precision, recall, and other performance indicators.
2. Data Collection
• Relevant data is collected, often representing examples or instances of the problem that the machine learning system aims to address. This data is used to train and test the algorithms.
4. Prediction or Inference
Once trained and evaluated, the machine learning model can make predictions or decisions when presented with new, unseen data. The model generalizes its learning to make informed responses based on patterns identified during training.