{"id":228994,"date":"2024-02-26T15:29:22","date_gmt":"2024-02-26T14:29:22","guid":{"rendered":"https:\/\/blog.lewagon.com\/?p=228994"},"modified":"2024-02-29T14:51:18","modified_gmt":"2024-02-29T13:51:18","slug":"algorithme-machine-learning","status":"publish","type":"post","link":"https:\/\/blog.lewagon.com\/fr\/data\/algorithme-machine-learning\/","title":{"rendered":"Qu&rsquo;est ce qu&rsquo;un algorithme de Machine Learning ?"},"content":{"rendered":"","protected":false},"excerpt":{"rendered":"<p>Que se cache-t-il derri\u00e8re le machine learning ? Ce sous-domaine de l&rsquo;intelligence artificielle est omnipr\u00e9sent dans notre quotidien. D\u00e9cryptez les algorithmes de machine learning et d\u00e9couvrez comment apprennent les machines.  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le fonctionnement d'un algorithme de machine learning : types, calculs et applications, explorez les bases du machine learning.","_yoast_wpseo_title":null,"contenu_de_larticle":"<h2>D\u00e9finition du machine learning<\/h2>\r\nL'une des premi\u00e8res d\u00e9finitions du <a href=\"https:\/\/blog.lewagon.com\/fr\/skills\/comprendre-le-machine-learning-guide-complet\/\" target=\"_blank\" rel=\"noopener\">machine learning<\/a> tel que nous le connaissons aujourd'hui remonte \u00e0 1959, lorsque Arthur Samuel, <span style=\"color: #000000;\">l'un des pionniers am\u00e9ricains de l'IA et du Machine Learning<\/span>, a d\u00e9clar\u00e9 que :\r\n<blockquote><strong>Le machine learning est le domaine d'\u00e9tude qui donne aux ordinateurs la capacit\u00e9 d'apprendre sans \u00eatre explicitement programm\u00e9s.<\/strong><\/blockquote>\r\nMais que signifie avoir un programme qui n'est pas \"explicitement programm\u00e9\" ? Il est \u00e9vident qu'il y a du code (beaucoup de code) impliqu\u00e9 dans le machine learning. Alors, quelle est la diff\u00e9rence entre la programmation classique et la programmation du machine learning ?\r\n\r\nImaginons qu'un(e) d\u00e9veloppeur(se) classique et un(e) ing\u00e9nieur(e) en machine learning d\u00e9cident de construire un programme simple : un programme qui dessine une pomme rouge.\r\n\r\nLe ou la d\u00e9veloppeur(se) donnerait au programme <strong>des instructions pr\u00e9cises sur la fa\u00e7on de dessiner une pomme<\/strong> : dessiner un cercle, colorier l'int\u00e9rieur en rouge, dessiner une petite feuille verte au sommet du cercle...\r\n\r\nL'ing\u00e9nieur(e) en machine learning, quant \u00e0 lui, <strong>fournirait plut\u00f4t au programme des donn\u00e9es : des images de pommes rouges<\/strong>. Avec suffisamment d'exemples, le mod\u00e8le apprendrait alors \u00e0 quoi ressemble une pomme : la forme, la couleur, tous les petits d\u00e9tails. Avec suffisamment de donn\u00e9es, il serait m\u00eame capable de dessiner une nouvelle pomme.\r\n\r\nVous pouvez essayer vous-m\u00eame ! Pouvez-vous nous dire ce que sera le prochain objet dans l'image ci-dessous ?\r\n\r\n<img class=\"alignnone size-large wp-image-228820\" src=\"https:\/\/blog.lewagon.com\/wp-content\/uploads\/2022\/06\/algorithme-machine-learning-1-1024x131.png\" alt=\"algorithme de machine learning\" width=\"800\" height=\"102\" \/>\r\n\r\n&nbsp;\r\n\r\nSi vous avez devin\u00e9 qu'un cercle bleu vient ensuite, f\u00e9licitations, vous \u00eates aussi dou\u00e9(e) qu'un mod\u00e8le de machine learning !\r\n\r\nComment avez-vous fait cela ? Et bien, votre cerveau a examin\u00e9 les donn\u00e9es (les objets qui pr\u00e9c\u00e8dent le point d'interrogation). Il a ensuite absorb\u00e9 les caract\u00e9ristiques des objets (la forme et la couleur), et il a d\u00e9couvert un motif dans la s\u00e9quence de ces formes. Enfin, votre cerveau a suppos\u00e9 un algorithme pour pr\u00e9dire la forme suivante.\r\n\r\nDans cet article, nous allons justement explorer cet algorithme : les r\u00e8gles, la logique, et les calculs derri\u00e8re les d\u00e9cisions d'un mod\u00e8le de machine learning.\r\n\r\n&nbsp;\r\n<h2>Des algorithmes qui pr\u00e9disent tout<\/h2>\r\nLes <strong>mod\u00e8les<\/strong> <strong>de machine learning<\/strong> font des pr\u00e9dictions bas\u00e9es sur des donn\u00e9es. Les <strong>algorithmes de machine learning<\/strong> sont la mani\u00e8re dont les mod\u00e8les produisent ces sorties. Ce sont les \u00e9tapes de calcul effectu\u00e9es par le mod\u00e8le pour trouver les relations entre les donn\u00e9es, en examinant les motifs trouv\u00e9s entre les points de donn\u00e9es.\r\n\r\nLes algorithmes de machine learning peuvent \u00eatre divis\u00e9s en deux cat\u00e9gories :\r\n<ul>\r\n \t<li><strong>R\u00e9gression<\/strong> : des algorithmes qui aident \u00e0 pr\u00e9dire une valeur exacte (par exemple, quel sera le cours des actions d'Apple demain)<\/li>\r\n \t<li><strong>Classification<\/strong> : des algorithmes qui aident \u00e0 pr\u00e9dire une cat\u00e9gorie (par exemple, si le cours de l'action d'Apple sera plus \u00e9lev\u00e9 ou plus bas qu'aujourd'hui)<\/li>\r\n<\/ul>\r\nLe <strong>choix<\/strong> de votre algorithme d\u00e9pendra du <strong>type de probl\u00e8me<\/strong> que vous essayez de r\u00e9soudre, des <strong>ressources<\/strong> dont vous disposez, et de la <strong>taille et complexit\u00e9 des donn\u00e9es<\/strong> avec lesquelles vous travaillez.\r\n\r\n&nbsp;\r\n<h2>R\u00e9gression - \"Donne-moi un nombre\"<\/h2>\r\nLes <strong>algorithmes de r\u00e9gression<\/strong> permettent de pr\u00e9dire une valeur en examinant comment cette valeur d\u00e9pend d\u2019autres donn\u00e9es. L\u2019algorithme a donc pour but de faire une pr\u00e9diction de la variable cible ou de la variable \u00e0 expliquer (<em><strong>y<\/strong><\/em>), gr\u00e2ce \u00e0 des variables dites explicatives ou pr\u00e9dictives (<em><strong>x<\/strong><\/em>).\r\n\r\nAvec un algorithme de r\u00e9gression, on pourrait par exemple pr\u00e9dire la temp\u00e9rature de l'air, le salaire des employ\u00e9s, le cours de certaines actions, le nombre de \"likes\" sur un post Instagram, etc.\r\n\r\nPrenons l\u2019exemple de l'algorithme de <strong>r\u00e9gression lin\u00e9aire<\/strong>, qui tente de pr\u00e9dire la variable cible comme une combinaison lin\u00e9aire des variables explicatives.\r\n\r\nAdmettons que nous voulons pr\u00e9dire la taille de quelqu'un, en connaissant sa pointure de chaussure. Nous collectons des donn\u00e9es qui, lorsqu'elles sont repr\u00e9sent\u00e9es sur un graphique, ressemblent \u00e0 ceci :\r\n\r\n<img class=\" wp-image-228826 aligncenter\" src=\"https:\/\/blog.lewagon.com\/wp-content\/uploads\/2022\/06\/algorithme-machine-learning-2-1024x629.png\" alt=\"algorithme machine learning\" width=\"699\" height=\"429\" \/>La formule au c\u0153ur de l'algorithme de r\u00e9gression lin\u00e9aire est tr\u00e8s simple, vous l\u2019avez tr\u00e8s probablement d\u00e9j\u00e0 rencontr\u00e9e \u00e0 l\u2019\u00e9cole.\r\n\r\n<strong><em>y<\/em> = <\/strong>a<strong> * <em>x<\/em> + <\/strong>b\r\n\r\nOu dans notre cas :\r\n\r\n<em><strong>Taille<\/strong><\/em> = a * <em><strong>Pointure<\/strong><\/em> + b\r\n\r\nCela signifie que la taille est directement et proportionnellement li\u00e9e \u00e0 la pointure de chaussure d'une personne, autrement dit lin\u00e9airement li\u00e9e.\r\n\r\nAvec ces informations, le mod\u00e8le de machine learning trouve les meilleurs <em><strong>a<\/strong><\/em> et <em><strong>b<\/strong><\/em> \u00e0 partir de la formule ci-dessus pour tracer la droite de r\u00e9gression qui s'ajuste le mieux \u00e0 toutes les donn\u00e9es collect\u00e9es :\r\n\r\n<img class=\" wp-image-228832 aligncenter\" src=\"https:\/\/blog.lewagon.com\/wp-content\/uploads\/2022\/06\/algorithme-machine-learning-3-1024x629.png\" alt=\"\" width=\"699\" height=\"429\" \/>Le mod\u00e8le de machine learning apprend les <em><strong>a<\/strong><\/em> et les <em><strong>b<\/strong><\/em> qui d\u00e9terminent cette droite en essayant de nombreuses possibilit\u00e9s et en v\u00e9rifiant \u00e0 chaque fois \u00e0 quelle distance est la ligne pour tous les points de donn\u00e9es. Le mod\u00e8le s\u2019arr\u00eate lorsqu'il trouve les coefficients a et b pour lesquels la distance entre la droite de r\u00e9gression et chaque point est la plus faible.\r\n\r\nUne fois que le mod\u00e8le a connaissance de la relation entre la pointure de chaussure et la taille, lorsque nous lui donnons une nouvelle donn\u00e9e, la pointure de chaussure d\u2019un ami par exemple. Il utilise alors la formule qu'il a apprise pour pr\u00e9dire la taille de notre ami :\r\n\r\n<img class=\" wp-image-228838 aligncenter\" src=\"https:\/\/blog.lewagon.com\/wp-content\/uploads\/2022\/06\/algorithme-machine-learning-4-1024x629.png\" alt=\"algorithme machine learning\" width=\"701\" height=\"430\" \/>\r\n\r\n&nbsp;\r\n\r\n&nbsp;\r\n<h2>Classification - \"Donne-moi une cat\u00e9gorie\"<\/h2>\r\nSi les algorithmes de r\u00e9gression nous aident \u00e0 pr\u00e9dire une valeur sur un spectre continu, les <strong>algorithmes de classification<\/strong> permettent d\u2019attribuer des cat\u00e9gories distinctes \u00e0 nos donn\u00e9es.\r\n\r\nVous utiliseriez des algorithmes de classification si vous deviez pr\u00e9dire la capacit\u00e9 d\u2019un client de banque \u00e0 rembourser un pr\u00eat ou non, pour s\u00e9parer les photos de chats de photos de chiens, pour d\u00e9cider si les taches sur une radio sont dangereuses ou non, ou pour distinguer les avis clients n\u00e9gatifs des avis positifs.\r\n\r\nL\u2019exemple d\u2019algorithme de classification le plus connu est l'<strong>algorithme des <em>K<\/em> plus proches voisins (KNN)<\/strong>. Voyons comment il pourrait nous aider \u00e0 distinguer nos animaux de compagnie pr\u00e9f\u00e9r\u00e9s.\r\n\r\nDisons que nous avons collect\u00e9 un tas d'informations sur les chiens et les chats, par exemple la taille de leurs pattes et la longueur de leurs oreilles. Nous obtenons le graphique suivant :\r\n\r\n<img class=\" wp-image-228844 aligncenter\" src=\"https:\/\/blog.lewagon.com\/wp-content\/uploads\/2022\/06\/algorithme-machine-learning-5-1024x629.png\" alt=\"algorithme machine learning\" width=\"699\" height=\"429\" \/>\r\n\r\n&nbsp;\r\n\r\nPour classer de nouveaux animaux, l'algorithme KNN \u00e9valuera la distance aux <em><strong>K<\/strong><\/em> voisins les plus proches et classera l'animal en fonction de la majorit\u00e9 de ses voisins. Par exemple :\r\n\r\n<img class=\"alignnone wp-image-229006\" src=\"https:\/\/blog.lewagon.com\/wp-content\/uploads\/2024\/02\/algorithme-machine-learning-6.png\" alt=\"\" width=\"429\" height=\"266\" \/><img class=\"alignnone wp-image-229012\" src=\"https:\/\/blog.lewagon.com\/wp-content\/uploads\/2024\/02\/algorithme-machine-learning-6-copie.png\" alt=\"\" width=\"475\" height=\"283\" \/>\u00a0<img class=\"wp-image-229018 alignnone\" src=\"https:\/\/blog.lewagon.com\/wp-content\/uploads\/2024\/02\/algorithme-machine-learning-6-copie-2.png\" alt=\"\" width=\"443\" height=\"267\" \/>\r\n\r\n&nbsp;\r\n\r\n&nbsp;\r\n\r\n&nbsp;\r\n<h2>Le machine learning est aussi simple que complexe<\/h2>\r\nL'intuition derri\u00e8re un algorithme de machine learning peut en r\u00e9alit\u00e9 \u00eatre assez simple. Avec des outils modernes, vous pourriez rapidement trouver des moyens d'am\u00e9liorer votre travail gr\u00e2ce au machine learning : c'est ce que nous enseignons dans notre <a href=\"https:\/\/hubs.li\/Q02mG2C00\" target=\"_blank\" rel=\"noopener\">formation de 40 heures sur Python et le Machine Learning<\/a>.\r\n\r\nM\u00eame si la quantit\u00e9 de donn\u00e9es augmente, une bonne partie des r\u00e8gles continue \u00e0 s\u2019appliquer.\r\n\r\nEssayons de pr\u00e9dire la taille d'une personne, non seulement \u00e0 partir de sa pointure de chaussure, mais aussi \u00e0 partir de la taille de sa main, de son \u00e2ge et de son poids ? Il va \u00eatre difficile de tracer une droite sur un graphique dans ce cas l\u00e0, d\u2019abord parce que le graphique serait en 4D !\r\n\r\nMais pour un mod\u00e8le de machine learning, pr\u00e9dire une valeur \u00e0 partir d'une seule variable ou de cent variables fonctionnerait toujours de la m\u00eame mani\u00e8re.\r\n\r\nC'est d'ailleurs pourquoi les professionnel(le)s du machine learning doivent r\u00e9fl\u00e9chir \u00e0 d\u2019autres questions pour construire des mod\u00e8les vraiment impactants :\r\n<h2>Quel sera l\u2019impact sur le business ?<\/h2>\r\n<a href=\"https:\/\/blog.lewagon.com\/fr\/skills\/lintelligence-artificielle-decryptee-reponses-aux-questions-sur-lia\/\" target=\"_blank\" rel=\"noopener\">L'IA et le machine learning sont au c\u0153ur de toutes les discussions<\/a>, et ces mots sont parfois utilis\u00e9s pour attirer des individus vers de nouveaux produits. Alors avant toute chose, il est important de <strong>r\u00e9fl\u00e9chir \u00e0 l\u2019impact que le machine learning pourrait avoir sur votre travail<\/strong>. Les algorithmes de machine learning consistent \u00e0 cr\u00e9er une sortie en recherchant des motifs : si vous essayez d'appliquer le machine learning \u00e0 n'importe quel ensemble de donn\u00e9es ou probl\u00e9matique business qui n'est pas r\u00e9p\u00e9titif, vous risquez probablement d'\u00eatre d\u00e9\u00e7u(e) par les r\u00e9sultats.\r\n<h2>Quel type de risque est acceptable ?<\/h2>\r\nIl y a pratiquement toujours <strong>un certain niveau d\u2019erreur ou de risque<\/strong> dans les pr\u00e9dictions des mod\u00e8les de machine learning, et le compromis entre l'exactitude du mod\u00e8le et les ressources n\u00e9cessaires \u00e0 son apprentissage est tr\u00e8s important. Si vous essayez d'int\u00e9grer des recommandations de produits dans votre boutique en ligne, vous serez probablement satisfait(e) avec une pr\u00e9cision de 70%. Si vous cherchez \u00e0 pr\u00e9dire la bonne trajectoire d\u2019un vaisseau pour le faire revenir sur terre, 70% n\u2019est pas acceptable du tout !\r\n<h2>Y a-t-il des biais dans nos donn\u00e9es ?<\/h2>\r\nC\u2019est un sujet qui peut conduire \u00e0 des probl\u00e9matiques invisibles mais non n\u00e9gligeables. Pour <strong>construire des mod\u00e8les justes et \u00e9thiques<\/strong>, on ne peut pas se fier uniquement aux chiffres choisis par l'algorithme. Vous devez r\u00e9fl\u00e9chir \u00e0 ce qu\u2019impliquent les donn\u00e9es \u00e0 partir desquelles le mod\u00e8le apprend. Un mod\u00e8le pourrait cat\u00e9goriser comme chats des chiens si on lui fournit plus de photos de chats que de photos de chiens. Ce m\u00eame mod\u00e8le pourrait donc finir par ne pas d\u00e9tecter de mani\u00e8re \u00e9gale tous les types de visages humains, si les donn\u00e9es sont biais\u00e9es en termes de couleurs de peau ou de genre.\r\n\r\n&nbsp;\r\n\r\n&nbsp;\r\n\r\nLa construction de mod\u00e8les de machine learning doit \u00eatre supervis\u00e9e par des humains pour obtenir des r\u00e9sultats durables et responsables, des <a href=\"https:\/\/blog.lewagon.com\/fr\/career\/metiers-tech-data-science-machine-learning-engineer\/\" target=\"_blank\" rel=\"noopener\"><strong>machine learning engineers<\/strong><\/a> par exemple. Certains m\u00e9tiers comme <strong>responsable de l\u2019\u00e9thique de l\u2019IA<\/strong> commencent \u00e9galement \u00e0 faire leur apparition.\r\n\r\n&nbsp;\r\n\r\n<a href=\"https:\/\/hubs.li\/Q02mG2C00\" target=\"_blank\" rel=\"noopener\"><img class=\"alignnone size-full wp-image-229110\" src=\"https:\/\/blog.lewagon.com\/wp-content\/uploads\/2024\/02\/FR-Blog-visual-4.png\" alt=\"Formation Machine Learning et Python\" width=\"600\" height=\"400\" \/><\/a>\r\n\r\n&nbsp;\r\n\r\n<!-- notionvc: c7039b8b-e5d8-4255-b6cd-fb567fa68e37 -->","_contenu_de_larticle":"field_6412588b55814","titre_de_la_banniere":"","_titre_de_la_banniere":"field_6422eb732c237","paragraphe_de_la_banniere":"","_paragraphe_de_la_banniere":"field_6422ed834a968","intitule_du_bouton_de_la_banniere":"","_intitule_du_bouton_de_la_banniere":"field_6422ed934a969","lien_du_bouton_de_la_banniere":"","_lien_du_bouton_de_la_banniere":"field_6422eda54a96a","yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.5 (Yoast SEO v27.6) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Qu&#039;est ce qu&#039;un algorithme de Machine Learning ? | Blog Le Wagon<\/title>\n<meta name=\"description\" content=\"D\u00e9couvrez le fonctionnement d&#039;un algorithme de machine learning : types, calculs et applications, explorez les bases du machine learning.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/blog.lewagon.com\/fr\/data\/algorithme-machine-learning\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Qu&#039;est ce qu&#039;un algorithme de Machine Learning ?\" \/>\n<meta property=\"og:description\" content=\"D\u00e9couvrez le fonctionnement d&#039;un algorithme de machine learning : types, calculs et applications, explorez les bases du 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