Glossary

The Bias-Variance Tradeoff (ML) refers to balancing a model’s complexity or adaptability (variance) and its presuppositions regarding data (bias).

Big Data in AI refers to the vast amounts of structured and unstructured data necessary for training machine learning models, leading to better predictive accuracy.

Big O Notation is a mathematical concept used to gauge the efficiency or complexity of algorithms. It estimates the maximum number of operations needed as the input size increases, helping in analyzing algorithm performance.

Binary Classification is a vital concept within machine learning, aimed at categorizing given observations into one of two possible classes. This supervised learning approach is an essential tool for numerous applications in the data-driven world.

F-Score metrics (Accuracy, Precision, Recall, and F1 Score) assess classification model performance. Accuracy gauges correct predictions, Precision evaluates true positives, Recall denotes model sensitivity, and F1 Score is Precision and Recall’s harmonic mean.

AI or Artificial Intelligence is transforming our world, constituting machines that mimic cognitive human functions, such as learning, problem-solving, decision-making, and more. It’s a key player in the tech advancement field.