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Foundation Model (FM)

Foundation Model (FM)

Introduction The Foundation Model (FM) is a state-of-the-art language model developed by OpenAI. It is designed to generate human-like text and has been trained on a vast amount of internet… 

Training Data

Training Data

Introduction Training data is a crucial component in the development of artificial intelligence systems. It refers to the information or examples that are used to train an AI model or… 

True Negative

True Negative

Introduction Introduction: In the context of binary classification, a true negative refers to a situation where a model correctly predicts the absence of a particular condition or event when it… 

Explainability (XAI)

Explainability (XAI)

Introduction Explainability (XAI) refers to the ability of an AI system to provide understandable and transparent explanations for its decisions or actions. It aims to bridge the gap between the… 

Feature Selection

Feature Selection

Introduction Feature selection is a crucial step in machine learning and data analysis. It involves selecting a subset of relevant features from a larger set of available features to improve… 

Causal inference

Causal inference

Uncover the hidden connections with Causal inference. Introduction Causal inference is a branch of statistics and research methodology that aims to understand cause-and-effect relationships between variables. It involves determining whether… 

Virtual assistants (VAs)

Virtual assistants (VAs)

Introduction Virtual assistants (VAs) are computer programs or artificial intelligence (AI) systems designed to provide assistance and perform tasks for users. They are capable of understanding and responding to natural… 

Unsupervised Machine Learning

Unsupervised Machine Learning

Applications of Unsupervised Machine Learning in Anomaly Detection Unsupervised machine learning is a powerful tool that has gained significant attention in recent years. Unlike supervised learning, which requires labeled data…