ML algorithms are mathematical models that can be trained on data to perform a specific task,
such as classification, regression, clustering, or dimensionality reduction.
Their implementation involves several steps, including:
Data preparation: Cleaning, transforming, and preprocessing the data to make it suitable for training.
Model selection: Selecting an appropriate algorithm that fits the task and the characteristics of the data.
Model training: Using the selected algorithm and the preprocessed data to train the model and optimize its parameters.
Model evaluation: Measuring the performance of the model using metrics such as accuracy, precision, recall, and F1-score.
Model fine-tuning: Making adjustments to the model based on the evaluation results, such as changing the parameters or adding more data.
Deployment: Integrating the model into a production environment and making it available for use.