MOC: [[INTELLIGENCE ARTIFICIELLE]] Date : 2025-02-02 Auteur: [[Aurélien Géron]] Tags: #livre Note : *** ## Machine Learning ![[Machine Learning]] ## Data Mining ![[Data Mining]] ## Supervised Learning ![[Supervised Learning]] ## Classification ![[Classification]] ## Régression ![[Régression]] ## Feature ![[Feature]] ## Unsupervised Learning ![[Unsupervised Learning]] ## Clustering Algorithm ![[Clustering Algorithm]] ## Visualization Algorithm ![[Visualization Algorithm]] ## Dimensionality Reduction ![[Dimensionality Reduction]] ## Feature Extraction ![[Feature Extraction]] ## Anomaly Detection ![[Anomaly Detection]] ## Association Rule Learning ![[Association Rule Learning]] ## Semi-Supervised Learning ![[Semi-Supervised Learning]] ## Self-Supervised Learning ![[Self-Supervised Learning]] ## Reinforcement Learning ![[Reinforcement Learning]] ## Batch Learning ![[Batch Learning]] ## Data Drift ![[Data Drift]] ## Online Learning ![[Online Learning]] ## Learning Rate ![[Learning Rate]] ## Generalization ![[Generalization]] ## Instance-Based Learning ![[Instance-Based Learning]] ## Model-Based Learning ![[Model-Based Learning]] ## Sampling Bias ![[Sampling Bias]] ## Feature Engineering ![[Feature Engineering]] ## Overfitting ![[Overfitting]] ## Hyperparameter ![[Hyperparameter]] ## Underfitting ![[Underfitting]] ## Tester et valider un modèle ![[Tester et valider un modèle]] ## Pipeline Une pipelane dans le monde de la data est une séquence de composants qui analysent de la donnée à la suite les uns des autres. Cela nécessite du monitoring, c'est très similaire à un workflow d'automatisation.