Machine learning

APPRENTISSAGE NON-SUPERVISÉ avec Python (24/30)

UNSUPERVISED LEARNING with Python (24/30)

FEATURE SELECTION avec SKLEARN (23/30)

FEATURE SELECTION with SKLEARN (23/30)

SKLEARN IMPUTER : NETTOYAGE DE DONNÉES

SKLEARN IMPUTE: DATA CLEANING

SKLEARN PIPELINE AVANCÉE

SKLEARN ADVANCED PIPELINE

PYTHON SKLEARN PRE-PROCESSING + PIPELINE (22/30)

Sklearn make_scorer tutoriel : Créer vos propres métriques.

Sklearn make_scorer tutorial: Create your own metrics.

MÉTRIQUES de RÉGRESSIONS en DATA SCIENCE (Coefficient de Détermination, Erreur Quadratique, etc... )

REGRESSION METRICS in DATA SCIENCE (Coefficient of Determination, Squared Error, etc.)

CROSS-VALIDATION SKLEARN PYTHON (Techniques expliquées en Français)

SKLEARN PYTHON CROSS-VALIDATION (Techniques explained in French)

PYTHON SKLEARN - MODEL SELECTION : Train_test_split, Cross Validation, GridSearchCV (21/30)

PYTHON SKLEARN: KNN, LinearRegression et SUPERVISED LEARNING (20/30)

RÉGRESSION LINÉAIRE MULTIPLE AVEC NUMPY - ML#9

MULTIPLE LINEAR REGRESSION WITH NUMPY - ML#9

RÉGRESSION LINÉAIRE NUMPY - ML#8

NUMPY LINEAR REGRESSION - ML#8

PYTHON : L'ESSENTIEL POUR MACHINE LEARNING - ML#7

PYTHON: THE ESSENTIAL FOR MACHINE LEARNING - ML#7

RÉGRESSION LINÉAIRE (partie 2/2) - ML#6

LINEAR REGRESSION (part 2/2) - ML#6

MATRICES ET NUMPY - ML#5

DESCENTE DE GRADIENT (GRADIENT DESCENT) - ML#4

GRADIENT DESCENT - ML#4

LA RÉGRESSION LINÉAIRE (partie 1/2) - ML#3

LINEAR REGRESSION (part 1/2) - ML#3

Lecture 23 | MIT 6.832 (Underactuated Robotics), Spring 2019

Lecture 22 | MIT 6.832 (Underactuated Robotics), Spring 2019

COMMENT FONCTIONNE LE MACHINE LEARNING ?

HOW DOES MACHINE LEARNING WORK?

From Research to Scalable AI Solutions, 2 examples: Trust & Fairness and CNN Automatic Generation

Learning Embedding Space for Clustering From Deep Representations

A Survey of Attacks Against Twitter Spam Detectors in an Adversarial Environment

La théorie de l'apprentissage de Vapnik et les progrès récents de l'intelligence artificielle

Vapnik's learning theory and recent advances in artificial intelligence

Lecture 21 | MIT 6.832 (Underactuated Robotics), Spring 2018

Lecture 20 | MIT 6.832 (Underactuated Robotics), Spring 2018

Backpropagation, intuitively | Deep Learning Chapter 3

Backpropagation calculus | Deep Learning Chapter 4

Gradient descent, how neural networks learn | Deep Learning Chapter 2

BRAIN 2025, Prof. Terry Sejnowski

Jeu de go et intelligence artificielle — À chaud ! #2

Go game and artificial intelligence — Hot take! #2

Classification