Machine Learning (ML) is a discipline within the broader field of artificial intelligence (AI) that focuses on the development of algorithms and statistical models that enable computer systems to perform tasks by leveraging data-driven predictions or decisions, rather than being explicitly programmed for that task. Key technical aspects of ML include:

Training Data: At the core of ML is data. Algorithms learn from data, and the quality and quantity of this data can directly impact the performance of the model. This data is used to “train” the model.

Model: A model is a mathematical representation of a real-world process. In ML, a model is trained to recognize patterns using training data. Once trained, the model can make predictions or decisions without being specifically programmed to perform the task.

Features: These are individual measurable properties or characteristics of the phenomena being observed. They are input variables that help the model make predictions. Feature engineering, the process of selecting and transforming variables, is a crucial step in ML.

Algorithm: This refers to the specific computational procedure that is used to train a model. Different algorithms (e.g., linear regression, decision trees, neural networks) are suited for different types of tasks.

Loss Function: It quantifies how well the model’s predictions match the true values. During training, the objective is often to minimize this loss.

Training: The process by which the ML algorithm learns the patterns in the data. It involves feeding the algorithm a training dataset, adjusting the model parameters to minimize the loss, and iterating this process.

Evaluation: After training, models are evaluated on a separate set of data (test data) to gauge their performance. Common metrics include accuracy, precision, recall, and F1 score, among others, depending on the task.

Overfitting ∓ Regularization: Overfitting occurs when a model learns the training data too closely, including its noise and outliers, leading to poor performance on new, unseen data. Regularization techniques are used to prevent overfitting by adding a penalty to the loss function.

Supervised vs. Unsupervised Learning: In supervised learning, the algorithm is trained on labeled data, meaning the training data includes the answer key. In unsupervised learning, the algorithm is provided with unlabeled data and must find patterns and relationships within the data on its own.

Transfer Learning: A technique where a pre-trained model is fine-tuned for a different but related task. This is especially common in deep learning, where models trained on large datasets can be adapted to specific tasks with smaller datasets.