Machine Learning Definitions: A to Z Glossary Terms
Interested in machine learning but you keep seeing terms unfamiliar to you? This A-to-Z glossary defines key machine learning terms you need to know.
Machine learning professionals are immersed in the development, implementation, and upkeep of machine learning models and algorithms. They leverage diverse programming languages, frameworks, and libraries to build applications capable of learning from data, make predictions, and identify patterns. With a focus on testing and collaboration, machine learning experts play a pivotal role in creating intelligent systems that drive innovation across various industries and domains.
This machine learning glossary can be helpful if you want to get familiar with basic terms and advance your understanding of machine learning.
Machine Learning Definitions: A to Z Glossary Terms
Interested in machine learning but you keep seeing terms unfamiliar to you? This A-to-Z glossary defines key machine learning terms you need to know.
Machine learning professionals are immersed in the development, implementation, and upkeep of machine learning models and algorithms. They leverage diverse programming languages, frameworks, and libraries to build applications capable of learning from data, make predictions, and identify patterns. With a focus on testing and collaboration, machine learning experts play a pivotal role in creating intelligent systems that drive innovation across various industries and domains.
This machine learning glossary can be helpful if you want to get familiar with basic terms and advance your understanding of machine learning.
Machine Learning Terms
Algorithm
An algorithm is a set of rules or instructions machine learning models use to process data and make predictions or decisions. It is a crucial machine learning component as it defines the learning process.
Bias
Bias in machine learning refers to the tendency of a model to consistently favor specific outcomes or predictions over others due to the data it was trained on. Reducing bias is essential to ensure fair and accurate predictions.
Cross-Validation
Cross-validation is a technique used to assess the performance of a machine learning model by dividing the data into subsets and evaluating the model on different combinations of training and testing sets.
Deep Learning
Deep learning is a machine learning subfield that uses artificial neural networks to model and solve complex problems. Deep learning models are capable of learning hierarchical representations from data.
Ensemble Learning
Ensemble learning is a technique where multiple machine learning models are combined to improve prediction accuracy and reduce overfitting. Popular ensemble methods include bagging and boosting.
Feature Engineering
Feature engineering is the process of selecting, transforming, and creating relevant features from raw data to improve the performance of machine learning models.
Gradient Descent
Gradient descent is an optimization algorithm used to update the parameters of machine learning models during training. It aims to minimize the error or loss function and improve model performance.
Hyperparameters
Hyperparameters are a machine learning model's settings or configurations before training. They control the learning process and significantly impact model performance.
Imbalanced Data
Imbalanced data refers to a data set where the distribution of classes is significantly skewed, leading to an unequal number of instances for each class. Handling imbalanced data is essential to prevent biased model predictions.
Joint Probability
Joint probability is the probability of two or more events occurring simultaneously. In machine learning, joint probability is often used in modeling and inference tasks.
k-Nearest Neighbors (k-NN)
k-Nearest Neighbors is a simple and widely used classification algorithm that assigns a new data point to the majority class among its k nearest neighbors in the feature space.
Loss Function
A loss function measures the difference between the predicted and actual values in machine learning. It serves as the optimization objective for training the model.
Model Selection
Model selection is choosing the best machine learning model from a set of candidate models based on their performance metrics and generalization ability.
Normalization