Machine learning

Using linear regression to estimate non-linear problems (with code!)

Lecture 17: MIT 6.800/6.843 Robotics Manipulation (Fall 2021) | "Behavior Cloning"

Qiskit Falll Fest CIC-IPN Mexico 2021- Introducción al Aprendizaje de Máquina Cuántico

Qiskit Fall Fest CIC-IPN Mexico 2021- Introduction to Quantum Machine Learning

Machine Learning Practice: CoLab Interface

reading conditional independence from the BN

Gradient Descent | Neural Networks

Backpropagation Algorithm | Neural Networks

Sequential identification of agent reliability and the Bayes factor

Bayesian Networks independence I

Machine Learnia, La suite commence ici.

Machine Learnia, The sequel starts here.

[Extract] The UK's Quantum Computer webinar ::: Alexei Kondratyev - Quantum Machine Learning

VC dimension as an adversary to learning

Support Vector Machine | Face Detection

VC dimension I

explaining PAC

A simple example of PAC learning

Lecture 18 | MIT 6.881 (Robotic Manipulation), Fall 2020 | Reinforcement Learning (Part 2)

Lecture 17 | MIT 6.881 (Robotic Manipulation), Fall 2020 | Reinforcement Learning (Part 1)

Curse of dimensionality

Comment CHOISIR LE BON MODÈLE de Machine Learning ?

How to CHOOSE THE RIGHT Machine Learning MODEL?

QSI Seminar: Dr Markus Heyl, Max Planck Inst., Reinforcement Learning for Digital Quantum Simulation

Learning Parity with Noise|| @ CMU || Lecture 26b of CS Theory Toolkit

Union bound and ML theory

QSI Seminar: Asst Prof Nathan Wiebe, PNNL, Training Fully Quantum Boltzmann Machines, 29/05/2020

MODÈLE DE MACHINE LEARNING - Covid19 (29/30)

MACHINE LEARNING MODEL - Covid19 (29/30)

QSI Seminar: Asst. Prof. Nana Liu, SJTU, Introducing Adversarial Quantum Learning, 22/05/2020

ENSEMBLE LEARNING : BAGGING, BOOSTING et STACKING (25/30)

ENSEMBLE LEARNING: BAGGING, BOOSTING and STACKING (25/30)

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