Machine Learning

An ML pipeline is an automated sequence of steps that takes raw data through preparation, training, evaluation and deployment to a monitored machine learning model, making the process repeatable and reliable.

A loss function is a formula that measures how far a machine learning model's predictions are from the correct answers. Training works by adjusting the model to make this number as small as possible.

Gradient descent is an optimisation algorithm that trains machine learning models by repeatedly adjusting their parameters in the direction that most reduces the error, like walking downhill step by step to reach the lowest point.

Model variance measures how much a machine learning model's predictions would change if it were trained on a different sample of data. High variance means the model is overly sensitive to its training data and tends to overfit.

In machine learning, model bias usually means error caused by a model's overly simple assumptions, which makes it miss real patterns. The term is also used for unfair outcomes against certain groups, which is a separate but important problem.

Underfitting happens when a machine learning model is too simple or poorly trained to capture the real pattern in the data, so it performs badly on both training data and new data.

Overfitting happens when a machine learning model learns its training data too closely, including random noise, so it performs very well on training data but poorly on new, unseen data.

A test dataset is a portion of data kept completely separate from training and tuning, used once at the end to give an honest estimate of how a machine learning model will perform on new, real-world data.

A validation dataset is a portion of data held back from training and used to tune a machine learning model, compare options and decide when to stop training, before the final test on unseen data.

A training dataset is the portion of data used to teach a machine learning model. The model studies these examples and adjusts its internal parameters to learn the patterns that connect inputs to outputs.

Feature engineering is the process of turning raw data into input variables, called features, that help a machine learning model learn better, for example converting timestamps into day of week or combining columns into ratios.

Clustering is an unsupervised machine learning technique that groups similar data points together without using labels, helping reveal natural segments such as customer types, document topics or unusual behaviour.

Regression in machine learning is a supervised learning task where a model predicts a continuous number, such as a price, a temperature or a delivery time, based on patterns learned from labelled examples.

Classification in machine learning is a supervised learning task where a model learns to assign inputs to predefined categories, such as spam or not spam, by learning decision boundaries from labelled examples.

Self-supervised learning is a machine learning approach where a model creates its own training labels from raw data, for example by predicting a hidden word or the next word in a sentence. It is the main method behind large language models.