Machine Learning Glossary
Machine learning is a complex and rapidly-evolving field that involves a variety of technical terms and concepts. For those new to the field, it can be overwhelming to navigate through the jargon and understand the various terms used in machine learning. In this article, we'll provide a glossary of some of the most common terms used in machine learning, along with brief explanations to help you understand them.
Artificial Intelligence (AI): The field of computer science concerned with creating machines that can perform tasks that would typically require human intelligence.
Machine Learning (ML): A subset of AI where models learn the patterns in data by itself without rule-based interference.
Data: Any collection of facts or figures that can be analyzed to extract meaningful insights.
Dataset: A collection of data used to train a machine learning model.
Feature: A measurable property or characteristic of a dataset that is used to make predictions.
Algorithm: A set of rules or instructions for solving a problem, often used in machine learning to make predictions or classify data.
Model: A mathematical representation of a dataset that can be used to make predictions or classify data.
Training: The process of feeding a dataset into a machine learning model to teach it to make predictions.
Testing: The process of evaluating the accuracy of a machine learning model by testing it on a new dataset.
Validation: The process of testing a machine learning model on a separate dataset to ensure that it can generalize to new data.
Supervised learning: A type of machine learning where the model is trained on a labeled dataset, meaning that the input data is paired with the correct output.
Unsupervised learning: A type of machine learning where the model is trained on an unlabeled dataset, meaning that the input data is not paired with the correct output.
Classification: A machine learning model where the models tries to predict distinct categories such as email spam models.
Regression: A machine learning model where the model predicts continuous values such as stock price prediction.
Neural network: A machine learning model inspired by the structure of the human brain, consisting of layers of interconnected nodes.
Overfitting: A common problem in machine learning where the model becomes too complex and performs well on the training data but poorly on new data.
Underfitting: Another common problem where the model becomes too simple to catch the underlying patterns in datasets, and performs poorly on training and test set.
This glossary covers some of the most common terms used in machine learning, but there are many more technical terms and concepts to be aware of. By understanding these terms and concepts, you'll be better equipped to navigate the world of machine learning and start building your own models.
Artificial Intelligence (AI): The field of computer science concerned with creating machines that can perform tasks that would typically require human intelligence.
Machine Learning (ML): A subset of AI where models learn the patterns in data by itself without rule-based interference.
Data: Any collection of facts or figures that can be analyzed to extract meaningful insights.
Dataset: A collection of data used to train a machine learning model.
Feature: A measurable property or characteristic of a dataset that is used to make predictions.
Algorithm: A set of rules or instructions for solving a problem, often used in machine learning to make predictions or classify data.
Model: A mathematical representation of a dataset that can be used to make predictions or classify data.
Training: The process of feeding a dataset into a machine learning model to teach it to make predictions.
Testing: The process of evaluating the accuracy of a machine learning model by testing it on a new dataset.
Validation: The process of testing a machine learning model on a separate dataset to ensure that it can generalize to new data.
Supervised learning: A type of machine learning where the model is trained on a labeled dataset, meaning that the input data is paired with the correct output.
Unsupervised learning: A type of machine learning where the model is trained on an unlabeled dataset, meaning that the input data is not paired with the correct output.
Classification: A machine learning model where the models tries to predict distinct categories such as email spam models.
Regression: A machine learning model where the model predicts continuous values such as stock price prediction.
Neural network: A machine learning model inspired by the structure of the human brain, consisting of layers of interconnected nodes.
Overfitting: A common problem in machine learning where the model becomes too complex and performs well on the training data but poorly on new data.
Underfitting: Another common problem where the model becomes too simple to catch the underlying patterns in datasets, and performs poorly on training and test set.
This glossary covers some of the most common terms used in machine learning, but there are many more technical terms and concepts to be aware of. By understanding these terms and concepts, you'll be better equipped to navigate the world of machine learning and start building your own models.
Updated on: 20/02/2023
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