{"id":143,"date":"2026-02-06T08:27:29","date_gmt":"2026-02-06T08:27:29","guid":{"rendered":"https:\/\/pythonia.fr\/?page_id=143"},"modified":"2026-03-18T11:59:36","modified_gmt":"2026-03-18T11:59:36","slug":"comprendre-le-machine-learning","status":"publish","type":"page","link":"https:\/\/pythonia.fr\/index.php\/comprendre-le-machine-learning\/","title":{"rendered":"Machine Learning avec Python"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"143\" class=\"elementor elementor-143\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c9a112c e-grid e-con-boxed e-con e-parent\" data-id=\"c9a112c\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-f42f929 elementor-widget elementor-widget-heading\" data-id=\"f42f929\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Machine Learning avec Python<\/h2>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-eb7598e e-grid e-con-boxed e-con e-parent\" data-id=\"eb7598e\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-3a67df6 elementor-widget elementor-widget-heading\" data-id=\"3a67df6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><div style=\"display:flex;gap:40px;flex-wrap:wrap;font-family:sans-serif\">\n\n  <!-- Colonne gauche : Objectifs -->\n  <div style=\"flex:2;min-width:300px\">\n    <h3 style=\"color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px;font-size:16px\">\n      Objectifs :\n    <\/h3>\n    <ul style=\"font-size:14px;color:#FFFFFF;line-height:2;margin-left:20px\">\n      <li>Comprendre les principes fondamentaux du Machine Learning (supervis\u00e9, non supervis\u00e9, renforcement)<\/li>\n      <li>Pr\u00e9parer et transformer des donn\u00e9es pour l'entra\u00eenement de mod\u00e8les avec scikit-learn<\/li>\n      <li>Entra\u00eener et \u00e9valuer des mod\u00e8les de classification, r\u00e9gression et clustering<\/li>\n      <li>D\u00e9couvrir les bases du Deep Learning avec TensorFlow\/Keras<\/li>\n    <\/ul>\n  <\/div>\n\n  <!-- Colonne droite : Tarif + liens -->\n  <div style=\"flex:1;min-width:200px;background:#0B1D33;padding:25px;border-radius:12px;height:fit-content\">\n\n    <div style=\"padding:12px 0;border-bottom:1px solid #1a3a5c\">\n      <span style=\"color:#8899AA;font-size:12px;text-transform:uppercase;letter-spacing:0.5px\">Tarif inter \/ participant<\/span>\n      <div style=\"color:#FFFFFF;font-size:22px;font-weight:bold;margin-top:4px\">\n        2 500 \u20ac <span style=\"font-size:13px;font-weight:normal;color:#8899AA\">HT<\/span>\n      <\/div>\n    <\/div>\n\n    <a href=\"http:\/\/pythonia.fr\/wp-content\/uploads\/2026\/03\/programme_machine_learning-1.pdf\" target=\"_blank\" style=\"display:block;color:#FFFFFF;font-size:15px;text-decoration:none;padding:12px 0;border-bottom:1px solid #1a3a5c\">\n      \ud83d\udcc4 Programme (PDF)\n    <\/a>\n\n    <a href=\"#\" id=\"btn-dates\" style=\"display:block;color:#FFFFFF;font-size:15px;text-decoration:none;padding:12px 0;border-bottom:1px solid #1a3a5c\">\n      \ud83d\udcc5 Voir les dates\n    <\/a>\n\n    <button id=\"btn-contact\" style=\"display:block;width:100%;margin-top:20px;padding:15px 20px;background:#5DADE2;color:#FFFFFF;font-size:15px;font-weight:bold;border:none;border-radius:8px;cursor:pointer\">\n      \u2709\ufe0f Demande d'information\n    <\/button>\n\n  <\/div>\n<\/div><\/h2>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-13213e0 e-grid e-con-boxed e-con e-parent\" data-id=\"13213e0\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-da597ce elementor-widget elementor-widget-heading\" data-id=\"da597ce\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><div style=\"display:flex;gap:40px;flex-wrap:wrap\">\n\n  <!-- Colonne gauche : Contenu -->\n  <div style=\"flex:2;min-width:300px\">\n\n    <h3 style=\"font-size:24px;color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px\">Public vis\u00e9<\/h3>\n    <ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n      <li>Professionnels en reconversion avanc\u00e9e vers la data science<\/li>\n      <li>Analystes souhaitant comprendre les principes et la mise en \u0153uvre du Machine Learning avec Python, afin d'int\u00e9grer des projets d'ing\u00e9nierie ML ou d'analyse pr\u00e9dictive de donn\u00e9es (immobilier, finance).<\/li>\n    <\/ul>\n\n    <h3 style=\"font-size:24px;color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px\">Objectifs p\u00e9dagogiques<\/h3>\n    <ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n      <li>Comprendre les principes fondamentaux du Machine Learning (supervis\u00e9, non supervis\u00e9, renforcement)<\/li>\n      <li>Pr\u00e9parer et transformer des donn\u00e9es pour l'entra\u00eenement de mod\u00e8les avec scikit-learn<\/li>\n      <li>Entra\u00eener et \u00e9valuer des mod\u00e8les de r\u00e9gression (lin\u00e9aire, polynomiale, r\u00e9gularisation)<\/li>\n      <li>Entra\u00eener et \u00e9valuer des mod\u00e8les de classification (logistique, arbres de d\u00e9cision, Random Forest)<\/li>\n      <li>Appliquer des techniques de clustering (K-Means, DBSCAN, hi\u00e9rarchique)<\/li>\n      <li>D\u00e9couvrir les bases du Deep Learning avec TensorFlow\/Keras<\/li>\n      <li>R\u00e9aliser un pipeline ML complet de bout en bout sur des donn\u00e9es r\u00e9elles<\/li>\n    <\/ul>\n\n    <h3 style=\"font-size:24px;color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px\">Pr\u00e9requis<\/h3>\n    <ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n      <li>Connaissances de base en Python (variables, fonctions, boucles, structures de donn\u00e9es)<\/li>\n      <li>Notions \u00e9l\u00e9mentaires de math\u00e9matiques (statistiques descriptives, alg\u00e8bre de base)<\/li>\n      <li>Avoir suivi la formation Python D\u00e9butant ou justifier d'un niveau \u00e9quivalent<\/li>\n      <li>Disposer d'un ordinateur avec Python 3.x et acc\u00e8s internet<\/li>\n    <\/ul>\n\n    <h3 style=\"font-size:24px;color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px\">M\u00e9thodes p\u00e9dagogiques<\/h3>\n    <ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n      <li>Alternance de th\u00e9orie (40%) et de pratique (60%)<\/li>\n      <li>Expos\u00e9s interactifs, d\u00e9monstrations de mod\u00e8les ML en direct<\/li>\n      <li>TP individuels et en \u00e9quipe avec scikit-learn, TensorFlow\/Keras, Jupyter Notebook<\/li>\n      <li>P\u00e9dagogie active : r\u00e9solution de probl\u00e8mes collaboratifs, analyse de r\u00e9sultats en groupe<\/li>\n      <li>Supports de cours num\u00e9riques et fichiers d'exercices (acc\u00e8s p\u00e9renne)<\/li>\n    <\/ul>\n\n    <h3 style=\"font-size:24px;color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px\">Certification vis\u00e9e : RS6763 \u2013 Manipuler, analyser et visualiser des donn\u00e9es gr\u00e2ce aux modules Python de Data Science<\/h3>\n\n    <h1 style=\"font-size:24px;color:#FFFFFF;font-weight:bold;margin-top:40px;margin-bottom:20px\">Programme d\u00e9taill\u00e9<\/h1>\n\n    <h3 style=\"font-size:16px;color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px\">JOUR 1 \u2014 Fondamentaux du Machine Learning<\/h3>\n    <ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n      <li>Introduction au ML : d\u00e9finition, types d'apprentissage, applications<\/li>\n      <li>Apprentissage supervis\u00e9 vs non supervis\u00e9 vs par renforcement<\/li>\n      <li>Pipeline ML : donn\u00e9es \u2192 pr\u00e9paration \u2192 entra\u00eenement \u2192 \u00e9valuation \u2192 d\u00e9ploiement<\/li>\n      <li>Concepts cl\u00e9s : features, labels, mod\u00e8le, pr\u00e9diction<\/li>\n      <li>Biais et variance : underfitting, overfitting, compromis<\/li>\n      <li>Train\/test split : importance, stratification, validation crois\u00e9e<\/li>\n      <li>Introduction \u00e0 scikit-learn : installation, conventions, API<\/li>\n    <\/ul>\n    <p style=\"font-size:14px;color:#FFFFFF;margin-left:20px;margin-top:15px\"><strong>\u25a0 Travaux pratiques :<\/strong><br>\n    \u2192 TP1 : Exploration d'un dataset classique (Iris, Titanic)<br>\n    \u2192 TP2 : Premier mod\u00e8le de classification avec scikit-learn<br>\n    \u2192 TP3 : Analyse de l'impact du train\/test split sur les performances<\/p>\n\n    <h3 style=\"font-size:16px;color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px\">JOUR 2 \u2014 Pr\u00e9paration des donn\u00e9es et r\u00e9gression<\/h3>\n    <ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n      <li>Feature engineering : cr\u00e9ation, s\u00e9lection, importance des features<\/li>\n      <li>Gestion des valeurs manquantes : strat\u00e9gies d'imputation<\/li>\n      <li>Encodage des variables cat\u00e9gorielles : OneHot, Label, Target encoding<\/li>\n      <li>Normalisation et standardisation : MinMaxScaler, StandardScaler<\/li>\n      <li>R\u00e9gression lin\u00e9aire : principe, \u00e9quation, interpr\u00e9tation<\/li>\n      <li>R\u00e9gression polynomiale et r\u00e9gularisation (Ridge, Lasso)<\/li>\n      <li>M\u00e9triques de r\u00e9gression : MSE, RMSE, MAE, R\u00b2<\/li>\n    <\/ul>\n    <p style=\"font-size:14px;color:#FFFFFF;margin-left:20px;margin-top:15px\"><strong>\u25a0 Travaux pratiques :<\/strong><br>\n    \u2192 TP1 : Pipeline de pr\u00e9paration de donn\u00e9es complet<br>\n    \u2192 TP2 : Pr\u00e9diction de prix immobiliers (r\u00e9gression lin\u00e9aire)<br>\n    \u2192 TP3 : Comparaison des techniques de r\u00e9gularisation<\/p>\n\n    <h3 style=\"font-size:16px;color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px\">JOUR 3 \u2014 Classification et clustering<\/h3>\n    <ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n      <li>R\u00e9gression logistique : principe, fonction sigmo\u00efde, seuil<\/li>\n      <li>Arbres de d\u00e9cision : construction, crit\u00e8res, visualisation<\/li>\n      <li>Random Forest : ensemble learning, bagging, feature importance<\/li>\n      <li>M\u00e9triques de classification : accuracy, precision, recall, F1-score<\/li>\n      <li>Matrice de confusion et courbe ROC<\/li>\n      <li>K-Means clustering : principe, choix de k, m\u00e9thode du coude<\/li>\n      <li>DBSCAN et clustering hi\u00e9rarchique : comparaison<\/li>\n    <\/ul>\n    <p style=\"font-size:14px;color:#FFFFFF;margin-left:20px;margin-top:15px\"><strong>\u25a0 Travaux pratiques :<\/strong><br>\n    \u2192 TP1 : Classification de spam (emails) avec Random Forest<br>\n    \u2192 TP2 : Segmentation clients par clustering K-Means<br>\n    \u2192 TP3 : Comparaison de mod\u00e8les de classification sur un m\u00eame dataset<\/p>\n\n    <h3 style=\"font-size:16px;color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px\">JOUR 4 \u2014 Introduction au Deep Learning et projet final<\/h3>\n    <ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n      <li>R\u00e9seaux de neurones : perceptron, couches, activation<\/li>\n      <li>Architecture MLP : couches denses, forward propagation<\/li>\n      <li>Introduction \u00e0 TensorFlow\/Keras : installation, Sequential API<\/li>\n      <li>Entra\u00eenement : epochs, batch size, loss, optimizers<\/li>\n      <li>R\u00e9duction de dimension : PCA, t-SNE pour visualisation<\/li>\n      <li>Transfer learning : concept et cas d'usage<\/li>\n      <li>Sauvegarde et chargement de mod\u00e8les<\/li>\n    <\/ul>\n    <p style=\"font-size:14px;color:#FFFFFF;margin-left:20px;margin-top:15px\"><strong>\u25a0 Travaux pratiques :<\/strong><br>\n    \u2192 TP1 : Classification d'images MNIST avec r\u00e9seau de neurones<br>\n    \u2192 TP2 : Visualisation de donn\u00e9es haute dimension avec PCA\/t-SNE<br>\n    \u2192 TP3 (Projet final) : Pipeline ML complet de bout en bout sur donn\u00e9es r\u00e9elles<\/p>\n\n    <h3 style=\"font-size:16px;color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px\">\u00c9valuation<\/h3>\n    <ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n      <li>Test de positionnement en d\u00e9but de formation<\/li>\n      <li>\u00c9valuations formatives : exercices pratiques corrig\u00e9s, analyse de r\u00e9sultats en groupe, QCM interm\u00e9diaires<\/li>\n      <li>QCM final de 30 questions (Jour 4) \u2014 crit\u00e8re de r\u00e9ussite : 60%<\/li>\n      <li>Attestation de fin de formation d\u00e9livr\u00e9e<\/li>\n    <\/ul>\n\n  <\/div>\n<\/div><\/h2>\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-230aa62 e-grid e-con-full e-con e-child\" data-id=\"230aa62\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-3ec33fd elementor-widget elementor-widget-heading\" data-id=\"3ec33fd\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><div style=\"display:flex;gap:40px;flex-wrap:wrap\">\n\n\n\n<\/div><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-398c212 e-flex e-con-boxed e-con e-parent\" data-id=\"398c212\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Machine Learning avec Python Objectifs : Comprendre les principes fondamentaux du Machine Learning (supervis\u00e9, non supervis\u00e9, renforcement) Pr\u00e9parer et transformer [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"site-sidebar-layout":"no-sidebar","site-content-layout":"","ast-site-content-layout":"full-width-container","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"disabled","ast-breadcrumbs-content":"","ast-featured-img":"disabled","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center 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