Mini-Batch

Ridge Regression

A regularised regression technique that adds an L2 penalty term to prevent overfitting by constraining coefficient magnitudes.

Elastic Net

A regularisation technique combining L1 and L2 penalties, balancing feature selection and coefficient shrinkage.

Cross-Validation

A resampling technique that partitions data into subsets, training on some and validating on others to assess model generalisation.

Overfitting

When a model learns the training data too well, including noise, resulting in poor performance on unseen data.

Underfitting

When a model is too simple to capture the underlying patterns in the data, resulting in poor performance on both training and test data.

Bias-Variance Tradeoff

The balance between a model's ability to minimise bias (error from assumptions) and variance (sensitivity to training data fluctuations).

Regularisation

Techniques that add constraints or penalties to a model to prevent overfitting and improve generalisation to new data.

Gradient Descent

An optimisation algorithm that iteratively adjusts parameters in the direction of steepest descent of the loss function.

Stochastic Gradient Descent

A variant of gradient descent that updates parameters using a randomly selected subset of training data each iteration.

Adam Optimiser

An adaptive learning rate optimisation algorithm combining momentum and RMSProp for efficient deep learning training.

Learning Rate

A hyperparameter that controls how much model parameters are adjusted with respect to the loss gradient during training.

Loss Function

A mathematical function that measures the difference between predicted outputs and actual target values during model training.

The process of converting a trained model into a format that can be stored, transferred, and later reconstructed for inference.

One complete pass through the entire training dataset during the machine learning model training process.

Density-Based Spatial Clustering of Applications with Noise — a clustering algorithm that finds arbitrarily shaped clusters based on density.

A partitioning algorithm that divides data into k clusters by minimising the distance between points and their cluster centroids.

Machine learning techniques that combine multiple models to produce better predictive performance than any single model, including bagging, boosting, and stacking approaches.

A recommendation technique that makes predictions based on the collective preferences and behaviour of many users.

The end-to-end automation of the machine learning pipeline including feature engineering, model selection, hyperparameter tuning, and deployment, making ML accessible to non-experts.

The algorithm for computing gradients of the loss function with respect to network weights, enabling neural network training.