Machine learning is a subfield of artificial intelligence (AI) that focuses on the development of algorithms and models that enable computers to learn patterns and make decisions or predictions based on data. A machine learning algorithm is a set of rules and statistical techniques that allows a computer system to perform a specific task without being explicitly programmed for that task.

Here's a high-level overview of how machine learning algorithms work:

• Data Collection: The first step in any machine learning process is to gather relevant data. This data serves as the input for the algorithm, and its quality and quantity play a crucial role in the performance of the model.

• Data Preprocessing: Raw data often requires cleaning and preprocessing to remove noise, handle missing values, and standardize formats. This step ensures that the data is suitable for training the machine learning model.

• Feature Extraction: Features are the variables or attributes in the dataset that the algorithm uses to make predictions. Feature extraction involves selecting the most relevant features or transforming existing ones to improve the model's accuracy.

• Model Training: In the training phase, the machine learning algorithm learns from the input data. The algorithm adjusts its internal parameters to minimize the difference between its predictions and the actual outcomes in the training data. This process involves optimization techniques to fine-tune the model.

• Model Evaluation: Once trained, the model needs to be evaluated on a separate set of data that it has never seen before. This testing dataset helps assess the model's generalization ability and its performance on new, unseen data.

• Prediction/Inference: After successful training and evaluation, the model is ready to make predictions or inferences on new, unseen data. The model uses the patterns it learned during training to make predictions about future or unseen instances.