Machine learning is a subset of artificial intelligence (AI) that focuses on developing algorithms that enable computers to learn from data, make predictions, and improve their performance over time. The field has gained significant importance due to the exponential growth of data, allowing businesses to automate processes and derive insights from vast amounts of information ⁴.

The fundamentals of machine learning include several key concepts and techniques. One of the primary categories is supervised learning, where algorithms are trained on labeled data, meaning each input has a corresponding output label. This allows the algorithm to learn patterns and make predictions on new, unseen data. In contrast, unsupervised learning deals with unlabeled data, where the algorithm identifies patterns and structures independently ⁴.

Another important aspect is feature engineering, which involves selecting and transforming relevant features in the input data to enhance algorithm performance. Model selection is also crucial, as it determines which type of machine learning algorithm to use based on the task and data complexity. Finally, evaluation is essential to measure the algorithm’s performance on test data, ensuring it generalizes well beyond the training set ⁴⁵.