A subset of artificial intelligence that enables computers to learn from data and make predictions without explicit programming.

A statistical method used to model the relationship between a dependent variable and one or more independent variables, often used for predictions.

The process of cleaning and transforming raw data into a suitable format for analysis and modeling, crucial for accurate predictions.

Using machine learning techniques to estimate the selling price of a house based on various features like location and size.

A type of machine learning where the model is trained on labeled data, meaning the input data is paired with the correct output.

A type of machine learning that deals with unlabeled data, allowing the model to identify patterns and relationships on its own.

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

The process of using domain knowledge to select, modify, or create features that make machine learning algorithms work better.

Data points that differ significantly from other observations, which can affect the performance of machine learning models.

Instances where data is not available for certain attributes, requiring techniques for imputation or removal.

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

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

Quantitative measures used to assess the performance of a machine learning model, such as accuracy, precision, and recall.

A common evaluation metric that measures the average of the squares of the errors, indicating how close predictions are to actual values.

The square root of MSE, providing an error measure in the same units as the original data, making it easier to interpret.

A statistical measure that indicates the proportion of variance in the dependent variable that can be explained by the independent variables.

The process of assessing the reliability and performance of a model using techniques like cross-validation.

A technique for assessing how the results of a statistical analysis will generalize to an independent data set.

The process of correcting or removing erroneous data from a dataset to improve its quality and accuracy.

Techniques applied to modify features to improve model performance, such as scaling or encoding.

The process of using data and statistical algorithms to predict future outcomes based on historical data.

A document that provides instructions on how to use a machine learning model, detailing its features and limitations.

Considerations regarding the moral aspects of machine learning, including bias, fairness, and transparency.

A collection of work that showcases a student's skills and projects, often used for job applications.

A high-level programming language commonly used in data science and machine learning for its simplicity and versatility.