{"id":5023,"date":"2023-03-24T15:45:41","date_gmt":"2023-03-24T15:45:41","guid":{"rendered":"http:\/\/skimai.com\/?p=5023"},"modified":"2024-08-31T16:36:29","modified_gmt":"2024-08-31T21:36:29","slug":"les-differents-types-dapprentissage-automatique","status":"publish","type":"post","link":"https:\/\/skimai.com\/fr\/different-types-of-machine-learning\/","title":{"rendered":"Les diff\u00e9rents types d'apprentissage automatique"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_1 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table des mati\u00e8res<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table des mati\u00e8res\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewbox=\"0 0 24 24\" version=\"1.2\" baseprofile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1' ><li class='ez-toc-page-1 ez-toc-heading-level-1'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/skimai.com\/fr\/different-types-of-machine-learning\/#Different_Types_of_Machine_Learning\" >Les diff\u00e9rents types d'apprentissage automatique<\/a><ul class='ez-toc-list-level-2' ><li class='ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/skimai.com\/fr\/different-types-of-machine-learning\/#Supervised_Learning\" >Apprentissage supervis\u00e9<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/skimai.com\/fr\/different-types-of-machine-learning\/#Unsupervised_Learning\" >Apprentissage non supervis\u00e9<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/skimai.com\/fr\/different-types-of-machine-learning\/#Semi-Supervised_Learning\" >Apprentissage semi-supervis\u00e9<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/skimai.com\/fr\/different-types-of-machine-learning\/#Reinforcement_Learning\" >Apprentissage par renforcement<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/skimai.com\/fr\/different-types-of-machine-learning\/#Transforming_Industries\" >Transformer les industries<\/a><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n<h1><span class=\"ez-toc-section\" id=\"Different_Types_of_Machine_Learning\"><\/span>Les diff\u00e9rents types d'apprentissage automatique<span class=\"ez-toc-section-end\"><\/span><\/h1>\n<p>L'apprentissage automatique est un domaine qui \u00e9volue rapidement et qui a le potentiel de transformer de nombreux secteurs, des soins de sant\u00e9 \u00e0 la finance en passant par la fabrication. L'apprentissage automatique repose sur quatre grands types de techniques d'apprentissage : <strong>apprentissage supervis\u00e9, apprentissage non supervis\u00e9, apprentissage semi-supervis\u00e9,<\/strong> et <strong>apprentissage par renforcement<\/strong>. <\/p>\n<\/p>\n<p>Chacune de ces approches a ses propres forces et faiblesses, et il est essentiel de comprendre comment elles fonctionnent pour r\u00e9ussir la mise en \u0153uvre de solutions d'intelligence artificielle (IA).<\/p>\n<p>*Avant de vous plonger dans ce blog sur l'apprentissage automatique, n'oubliez pas de consulter notre article sur l'IA et l'apprentissage automatique pour conna\u00eetre la diff\u00e9rence entre les deux.<\/p>\n<p><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Supervised_Learning\"><\/span>Apprentissage supervis\u00e9<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><img decoding=\"async\" src=\"http:\/\/skimai.com\/wp-content\/uploads\/2023\/03\/230313-4-Types-of-Machine-Learning-1.jpg\" alt=\"\" width=\"1200\" height=\"627\"><\/p>\n<p>L'apprentissage supervis\u00e9 est un type d'apprentissage automatique dans lequel l'algorithme est form\u00e9 sur un ensemble de donn\u00e9es \u00e9tiquet\u00e9es. Cela signifie que les donn\u00e9es d'entr\u00e9e ont d\u00e9j\u00e0 \u00e9t\u00e9 class\u00e9es ou \u00e9tiquet\u00e9es par des humains et que l'algorithme apprend \u00e0 faire des pr\u00e9dictions sur la base de ces donn\u00e9es \u00e9tiquet\u00e9es. Dans l'apprentissage supervis\u00e9, l'algorithme re\u00e7oit \u00e0 la fois les donn\u00e9es d'entr\u00e9e et les donn\u00e9es de sortie correspondantes, et il utilise ces informations pour apprendre une fonction de correspondance entre les deux.<\/p>\n<p>L'une des applications les plus courantes de l'apprentissage supervis\u00e9 est la classification. Dans la classification, l'algorithme est entra\u00een\u00e9 \u00e0 pr\u00e9dire la cat\u00e9gorie \u00e0 laquelle appartient un point de donn\u00e9es d'entr\u00e9e. Par exemple, un algorithme d'apprentissage supervis\u00e9 peut \u00eatre entra\u00een\u00e9 sur un ensemble de donn\u00e9es d'images de chats et de chiens, chaque image \u00e9tant \u00e9tiquet\u00e9e comme \"chat\" ou \"chien\". Une fois entra\u00een\u00e9, l'algorithme peut alors prendre une nouvelle image et pr\u00e9dire s'il s'agit d'un chat ou d'un chien.<\/p>\n<p>Une autre application courante de l'apprentissage supervis\u00e9 est la r\u00e9gression. Dans la r\u00e9gression, l'algorithme est entra\u00een\u00e9 \u00e0 pr\u00e9dire un r\u00e9sultat num\u00e9rique continu sur la base des donn\u00e9es d'entr\u00e9e. Par exemple, un algorithme d'apprentissage supervis\u00e9 pourrait \u00eatre entra\u00een\u00e9 sur un ensemble de donn\u00e9es relatives aux prix des maisons, chaque point de donn\u00e9es comprenant des informations telles que la taille de la maison, le nombre de chambres et l'emplacement. L'algorithme apprendrait alors \u00e0 pr\u00e9dire le prix d'une nouvelle maison sur la base de ces caract\u00e9ristiques.<\/p>\n<p><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Unsupervised_Learning\"><\/span>Apprentissage non supervis\u00e9<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><img decoding=\"async\" src=\"http:\/\/skimai.com\/wp-content\/uploads\/2023\/03\/230313-4-Types-of-Machine-Learning-2.jpg\" alt=\"\" width=\"1200\" height=\"627\"><\/p>\n<p>L'apprentissage non supervis\u00e9 est un autre type courant d'apprentissage automatique o\u00f9, contrairement \u00e0 l'apprentissage supervis\u00e9, l'algorithme est form\u00e9 sur un ensemble de donn\u00e9es non \u00e9tiquet\u00e9es. Dans l'apprentissage non supervis\u00e9, l'algorithme ne re\u00e7oit aucune information sur la sortie ou les \u00e9tiquettes des donn\u00e9es d'entr\u00e9e. Au lieu de cela, il apprend \u00e0 identifier des mod\u00e8les et des structures dans les donn\u00e9es par lui-m\u00eame.<\/p>\n<p>L'une des applications les plus courantes de l'apprentissage non supervis\u00e9 est le regroupement. Les algorithmes de clustering regroupent des points de donn\u00e9es similaires sur la base de leurs caract\u00e9ristiques, sans connaissance pr\u00e9alable des \u00e9tiquettes des donn\u00e9es. Cela peut \u00eatre utile pour des t\u00e2ches telles que la segmentation de la client\u00e8le, lorsqu'une entreprise souhaite regrouper des clients en fonction de leurs habitudes d'achat ou d'autres comportements.<\/p>\n<p>Une autre application de l'apprentissage non supervis\u00e9 est la r\u00e9duction de la dimensionnalit\u00e9. Les algorithmes de r\u00e9duction de la dimensionnalit\u00e9 sont utilis\u00e9s pour r\u00e9duire le nombre de caract\u00e9ristiques dans un ensemble de donn\u00e9es tout en pr\u00e9servant autant que possible les informations originales. Cela peut s'av\u00e9rer utile pour des t\u00e2ches telles que la reconnaissance d'images et de la parole, o\u00f9 les donn\u00e9es d'entr\u00e9e peuvent \u00eatre hautement dimensionnelles et difficiles \u00e0 traiter.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Semi-Supervised_Learning\"><\/span>Apprentissage semi-supervis\u00e9<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><img decoding=\"async\" src=\"http:\/\/skimai.com\/wp-content\/uploads\/2023\/03\/230313-4-Types-of-Machine-Learning-3.jpg\" alt=\"\" width=\"1200\" height=\"627\"><\/p>\n<p>L'apprentissage semi-supervis\u00e9 est une combinaison de techniques d'apprentissage supervis\u00e9 et non supervis\u00e9. L'algorithme est form\u00e9 sur un ensemble de donn\u00e9es qui contient \u00e0 la fois des donn\u00e9es \u00e9tiquet\u00e9es et non \u00e9tiquet\u00e9es.<\/p>\n<p>Les donn\u00e9es \u00e9tiquet\u00e9es sont utilis\u00e9es pour former l'algorithme de mani\u00e8re supervis\u00e9e, tandis que les donn\u00e9es non \u00e9tiquet\u00e9es sont utilis\u00e9es pour aider l'algorithme \u00e0 en apprendre davantage sur la structure sous-jacente des donn\u00e9es. L'id\u00e9e derri\u00e8re l'apprentissage semi-supervis\u00e9 est que les donn\u00e9es non marqu\u00e9es peuvent \u00eatre utilis\u00e9es pour am\u00e9liorer la pr\u00e9cision et la capacit\u00e9 de g\u00e9n\u00e9ralisation de l'algorithme.<\/p>\n<p>L'apprentissage semi-supervis\u00e9 est souvent utilis\u00e9 dans le traitement du langage naturel (NLP), un domaine de l'informatique et de l'intelligence artificielle qui vise \u00e0 permettre aux machines de comprendre le langage \u00e9crit et parl\u00e9 de la m\u00eame mani\u00e8re que les humains. Les mod\u00e8les de langage sont g\u00e9n\u00e9ralement form\u00e9s sur de grandes quantit\u00e9s de donn\u00e9es textuelles non \u00e9tiquet\u00e9es, qui peuvent \u00eatre utilis\u00e9es pour am\u00e9liorer la pr\u00e9cision de t\u00e2ches telles que la classification de textes et la traduction.<\/p>\n<p>L'apprentissage semi-supervis\u00e9 peut \u00e9galement \u00eatre utilis\u00e9 pour des t\u00e2ches telles que la reconnaissance d'images et de la parole, o\u00f9 la quantit\u00e9 de donn\u00e9es \u00e9tiquet\u00e9es peut \u00eatre limit\u00e9e ou co\u00fbteuse \u00e0 obtenir. En exploitant les donn\u00e9es non \u00e9tiquet\u00e9es disponibles, l'algorithme peut am\u00e9liorer ses performances pour la t\u00e2che en question.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Reinforcement_Learning\"><\/span>Apprentissage par renforcement<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><img decoding=\"async\" src=\"http:\/\/skimai.com\/wp-content\/uploads\/2023\/03\/230313-4-Types-of-Machine-Learning-4.jpg\" alt=\"\" width=\"1200\" height=\"627\"><\/p>\n<p>Le dernier grand type d'apprentissage automatique est l'apprentissage par renforcement, dans lequel un agent apprend \u00e0 prendre des d\u00e9cisions en interagissant avec un environnement. Dans l'apprentissage par renforcement, l'agent entreprend des actions dans l'environnement et re\u00e7oit un retour d'information sous forme de r\u00e9compenses ou de punitions. L'objectif de l'agent est d'apprendre \u00e0 maximiser sa r\u00e9compense \u00e0 long terme.<\/p>\n<p>Les applications les plus importantes de l'apprentissage par renforcement se trouvent dans le domaine de la robotique, o\u00f9 un agent peut apprendre \u00e0 contr\u00f4ler un robot physique pour qu'il effectue une t\u00e2che. L'agent agit dans l'environnement, par exemple en bougeant les bras ou les jambes du robot, et re\u00e7oit un retour d'information sous la forme d'une r\u00e9compense ou d'une p\u00e9nalit\u00e9 en fonction de la mani\u00e8re dont il ex\u00e9cute la t\u00e2che.<\/p>\n<p>L'apprentissage par renforcement peut \u00e9galement \u00eatre utilis\u00e9 pour les jeux et les simulations, o\u00f9 un agent peut apprendre \u00e0 jouer \u00e0 un jeu ou \u00e0 naviguer dans un environnement virtuel. Par exemple, l'apprentissage par renforcement a \u00e9t\u00e9 utilis\u00e9 pour former des agents \u00e0 jouer \u00e0 des jeux vid\u00e9o tels que les jeux Atari et le jeu de Go.<\/p>\n<p>Un autre domaine dans lequel l'apprentissage par renforcement s'est av\u00e9r\u00e9 prometteur est celui des soins de sant\u00e9, o\u00f9 il peut \u00eatre utilis\u00e9 pour optimiser les traitements de diverses maladies. L'agent peut apprendre \u00e0 prendre des d\u00e9cisions de traitement sur la base des donn\u00e9es du patient et recevoir un retour d'information sous la forme de r\u00e9sultats pour le patient.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Transforming_Industries\"><\/span>Transformer les industries<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>L'apprentissage automatique est un domaine qui \u00e9volue rapidement et qui a le potentiel de transformer presque tous les secteurs et de r\u00e9soudre certains de nos probl\u00e8mes les plus complexes. Comprendre les diff\u00e9rents types d'apprentissage automatique, tels que l'apprentissage supervis\u00e9, non supervis\u00e9, semi-supervis\u00e9 et par renforcement, nous permet de continuer \u00e0 repousser les limites et d'avoir un impact sur notre monde domin\u00e9 par les donn\u00e9es.<\/p>","protected":false},"excerpt":{"rendered":"<p>Different Types of Machine Learning Machine learning is a rapidly evolving field that has the potential to transform many industries, from healthcare to finance to manufacturing. At the core of machine learning are four main types of learning techniques: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Each of these approaches has its own [&hellip;]<\/p>\n","protected":false},"author":1003,"featured_media":8453,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"single-custom-post-template.php","format":"standard","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[67],"tags":[84],"class_list":["post-5023","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ml-nlp","tag-machine-learning"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v24.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Different Types of Machine Learning - Skim AI<\/title>\n<meta name=\"description\" content=\"In this article, we will discuss the different types of machine learning and how they are revolutionizing different industries for the better\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/skimai.com\/fr\/les-differents-types-dapprentissage-automatique\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Different Types of Machine Learning - 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