A subset of artificial intelligence where algorithms learn from data to make predictions or decisions without explicit programming.

A statistical technique used to predict future outcomes based on historical data, often utilizing machine learning algorithms.

A fundamental statistical method for modeling the relationship between a dependent variable and one or more independent variables.

The process of cleaning and transforming raw data into a usable format for analysis, ensuring quality and consistency.

The graphical representation of data to identify patterns, trends, and insights, enhancing understanding and decision-making.

An approach to analyzing data sets to summarize their main characteristics, often using visual methods.

The process of assessing a predictive model's performance using metrics like accuracy, precision, and recall.

A subset of data used to train a machine learning model, helping it learn patterns and relationships.

A separate subset of data used to evaluate the performance of a trained machine learning model.

A modeling error that occurs when a model learns noise in the training data instead of the underlying pattern.

A situation where a model is too simple to capture the underlying trend of the data, leading to poor performance.

A metric used to measure the differences between predicted and actual values, indicating model accuracy.

A statistical measure that represents the proportion of variance for a dependent variable that's explained by independent variables.

The process of identifying and correcting errors or inconsistencies in data to improve its quality.

Data points that are not recorded or available in a dataset, often requiring special handling during analysis.

Data points that differ significantly from other observations, potentially indicating variability or errors in the data.

Origins of data that can be used for analysis, such as databases, APIs, or public datasets.

The process of selecting, modifying, or creating new features from raw data to improve model performance.

A set of rules or steps used to solve a problem or perform a task, particularly in the context of machine learning.

Settings or configurations used to control the learning process of machine learning algorithms.

A visual display of key metrics and data points, often used to communicate insights effectively.

Individuals or groups with an interest in the outcomes of a project, including data analysts, marketers, and clients.

The process of using a model to estimate unknown values based on known data.

The practical use of machine learning techniques in various industries, such as finance, healthcare, and marketing.