Data Science: A to Z

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A

• A/B Testing: A statistical way of comparing two versions of a product or feature in order to determine which performs better.
• Accuracy: The percentage of correct predictions made by a model.
• Artificial Intelligence (AI): The study and design of intelligent agents, which are systems that can reason, learn, and act autonomously.
• Association Rule Learning: A method for discovering relationships between variables in large datasets.

B

• Batch Learning: A type of machine learning where the model is trained on the entire dataset at once, rather than incrementally.
• Bias: A systematic error or deviation from the true value in a statistical analysis.
• Big Data: Very large datasets that may be too large or complex for traditional data processing techniques.
• Boosting: A type of ensemble method that combines the predictions of multiple weak models to create a strong model.

C

• Classification: A type of machine learning task that involves predicting a class label for a given input data.
• Clustering: A type of unsupervised learning that involves grouping data into clusters based on similarity.
• Correlation: A statistical measure of the relationship between two variables.
• Cross-Validation: A method for evaluating the performance of a model by dividing the data into training and testing sets and using each in turn as the holdout set.

D

• Decision Tree: A type of machine learning model that uses a tree-like structure to make predictions based on feature values.
• Deep Learning: A subfield of machine learning that involves training artificial neural networks on large datasets.
• Dimensionality Reduction: The process of reducing the number of features in a dataset by selecting the most important ones or combining correlated features.
• DATAcated: Dedicated to data — see Kate Strachnyi :)

E

• Ensemble Method: A machine learning technique that combines the predictions of multiple models to create a more accurate prediction.
• Evaluation Metric: A measure used to evaluate the performance of a model.

F

• Feature: A characteristic or attribute of a data point that can be used to predict a target variable.
• Feature Engineering: The process of selecting and creating features to be used in a machine learning model.
• Feature Selection: The process of selecting a subset of relevant features to use in a model.

G

• Gradient Descent: An optimization algorithm used to find the values of parameters that minimize a loss function.

H

• Hyperparameter: A parameter that is set prior to training a machine learning model and controls the model’s behavior.

I

• Imbalanced Classes: A classification problem where one class significantly outnumbers the other classes.
• Imputation: The process of replacing missing or invalid data with a reasonable estimate.

K

• K-Fold Cross-Validation: A type of cross-validation where the data is divided into K folds, and the model is trained and evaluated K times, with a different fold used as the holdout set each time.
• K-Means Clustering: A type of clustering algorithm that divides the data into K clusters based on similarity.

L

• Label: The target variable in a supervised learning problem.
• Learning Rate: A hyperparameter that controls the step size at which the optimization algorithm makes updates to the model parameters.
• Loss Function: A measure of how well a model’s predictions match the ground truth labels.

M

• Machine Learning: A subfield of artificial intelligence that involves training models to make predictions or decisions based on data.
• Model: A mathematical representation of a process or system that can be used to make predictions or decisions.

N

• Neural Network: A type of machine learning model inspired by the structure and function of the brain, consisting of layers of interconnected nodes.

O

• Overfitting: A phenomenon that occurs when a model is excessively complex and has learned patterns in the training data that do not generalize to unseen data.

P

• Precision: The percentage of correct positive predictions made by a model.
• Predictor Variable: A feature used to predict a target variable in a machine learning model.

R

• Regression: A type of machine learning task that involves predicting a continuous target variable.
• Regularization: A technique used to prevent overfitting in machine learning models by adding a penalty term to the loss function.

S

• Sample: A subset of a population used to represent the entire population in statistical analysis.
• Supervised Learning: A type of machine learning where the model is trained on labeled data and learns to predict the label for new, unseen data.
• Support Vector Machine (SVM): A type of machine learning model that uses a hyperplane to classify data.

T

• Test Set: A subset of a dataset used to evaluate the performance of a machine learning model.
• Training Set: A subset of a dataset used to train a machine learning model.

U

• Unsupervised Learning: A type of machine learning where the model is not given any labeled training data and must discover patterns and relationships in the data on its own.

V

• Validation Set: A subset of a dataset used to tune the hyperparameters of a machine learning model.
• Variable: A characteristic or attribute that can vary or change within a dataset.

W

• Weight: In the context of machine learning, a weight is a parameter that determines the strength of the influence of an input feature on the output of the model.
• Weight Initialization: The process of setting the initial values for the weights of a neural network model.
• Weight Regularization: A technique used to prevent overfitting in neural network models by adding a penalty term to the loss function based on the magnitude of the weights.
• Weight Sharing: A technique used in neural networks to reduce the number of parameters and improve model performance. It involves sharing the same weights across multiple connections in the network.
• Word Embedding: A representation of words as vectors in a high-dimensional space, where semantically similar words are close together in the space.

X:

• XGBoost: A gradient boosting library for machine learning that is used to improve model performance.

Y

• YOLO (You Only Look Once): A real-time object detection algorithm that uses a convolutional neural network to predict the bounding boxes and class labels for objects in an image.

Z:

• Zero-Shot Learning: A type of machine learning where the model is able to classify objects it has never seen before, based on its knowledge of other objects and their relationships.