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| </head> |
| <body> |
| |
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| <a href="index.html" class="navbar-brand"> |
| <div class="brand-logo">ML</div> |
| <span>ML Academy</span> |
| </a> |
| <div class="navbar-nav"> |
| <a href="index.html" class="nav-link active"> |
| <span class="nav-icon">[H]</span> |
| <span>Accueil</span> |
| </a> |
| <a href="cours.html" class="nav-link"> |
| <span class="nav-icon">[C]</span> |
| <span>Cours</span> |
| </a> |
| <a href="tp.html" class="nav-link"> |
| <span class="nav-icon">[T]</span> |
| <span>TPs</span> |
| </a> |
| <a href="feedback.html" class="nav-link"> |
| <span class="nav-icon">[F]</span> |
| <span>Contact</span> |
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| <span>Google Colab Ready</span> |
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|
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| |
| <section class="hero"> |
| <div class="hero-bg"></div> |
| <div class="floating-shape shape-1"></div> |
| <div class="floating-shape shape-2"></div> |
| <div class="floating-shape shape-3"></div> |
| |
| <div class="hero-eyebrow"> |
| <div class="pulse"></div> |
| <span>Formation 2025/2026 — 100% Pratique</span> |
| </div> |
| |
| <h1 class="hero-title"> |
| Maitrisez le <span class="gradient-text">Machine Learning</span><br> |
| par la pratique |
| </h1> |
| |
| <p class="hero-subtitle"> |
| Apprenez les algorithmes ML essentiels a travers des projets concrets |
| avec des datasets reels de Kaggle. De zero a heros en quelques seances. |
| </p> |
| |
| <div class="hero-cta"> |
| <a href="cours.html" class="btn btn-primary btn-lg"> |
| Commencer les cours |
| </a> |
| <a href="tp.html" class="btn btn-secondary btn-lg"> |
| Voir les TPs |
| </a> |
| </div> |
| |
| <div class="hero-stats"> |
| <div class="hero-stat"> |
| <div class="hero-stat-value">8<span>+</span></div> |
| <div class="hero-stat-label">Algorithmes</div> |
| </div> |
| <div class="hero-stat"> |
| <div class="hero-stat-value">6<span></span></div> |
| <div class="hero-stat-label">TPs Complets</div> |
| </div> |
| <div class="hero-stat"> |
| <div class="hero-stat-value">4<span></span></div> |
| <div class="hero-stat-label">Datasets Reels</div> |
| </div> |
| <div class="hero-stat"> |
| <div class="hero-stat-value">0<span></span></div> |
| <div class="hero-stat-label">Installation</div> |
| </div> |
| </div> |
| </section> |
|
|
| |
| <section class="features"> |
| <div class="section-header scroll-animate"> |
| <div class="section-eyebrow">Pourquoi cette formation</div> |
| <h2 class="section-title">Ce que vous allez apprendre</h2> |
| <p class="section-subtitle"> |
| Une approche progressive alliant theorie solide et pratique intensive |
| sur des problemes reels du monde de la data science. |
| </p> |
| </div> |
| |
| <div class="features-grid"> |
| <div class="feature-card scroll-animate"> |
| <div class="feature-icon">D</div> |
| <h3 class="feature-title">Pretraitement des Donnees</h3> |
| <p class="feature-text"> |
| Maitrisez le nettoyage, la normalisation et l'ingenierie des features |
| pour preparer vos donnees a l'entrainement. |
| </p> |
| <div class="feature-tags"> |
| <span class="badge badge-primary">Pandas</span> |
| <span class="badge badge-secondary">NumPy</span> |
| <span class="badge badge-success">Scikit-learn</span> |
| </div> |
| </div> |
| |
| <div class="feature-card scroll-animate"> |
| <div class="feature-icon">A</div> |
| <h3 class="feature-title">Algorithmes de ML</h3> |
| <p class="feature-text"> |
| Comprendre et implementer les algorithmes fondamentaux : |
| Regression, Classification, Clustering et Deep Learning. |
| </p> |
| <div class="feature-tags"> |
| <span class="badge badge-primary">Regression</span> |
| <span class="badge badge-secondary">Random Forest</span> |
| <span class="badge badge-accent">Neural Networks</span> |
| </div> |
| </div> |
| |
| <div class="feature-card scroll-animate"> |
| <div class="feature-icon">E</div> |
| <h3 class="feature-title">Evaluation & Optimisation</h3> |
| <p class="feature-text"> |
| Apprenez a mesurer la performance de vos modeles et a les optimiser |
| avec les bonnes metriques et techniques. |
| </p> |
| <div class="feature-tags"> |
| <span class="badge badge-success">Cross-validation</span> |
| <span class="badge badge-warning">Grid Search</span> |
| <span class="badge badge-primary">Metriques</span> |
| </div> |
| </div> |
| |
| <div class="feature-card scroll-animate"> |
| <div class="feature-icon">P</div> |
| <h3 class="feature-title">Projets Concrets</h3> |
| <p class="feature-text"> |
| Travaillez sur des datasets reels de Kaggle : Titanic, Housing, |
| Iris et bien d'autres pour construire votre portfolio. |
| </p> |
| <div class="feature-tags"> |
| <span class="badge badge-success">Kaggle</span> |
| <span class="badge badge-accent">Portfolio</span> |
| <span class="badge badge-secondary">GitHub</span> |
| </div> |
| </div> |
| |
| <div class="feature-card scroll-animate"> |
| <div class="feature-icon">T</div> |
| <h3 class="feature-title">Series Temporelles</h3> |
| <p class="feature-text"> |
| Decouvrez les techniques specifiques pour predire des donnees |
| temporelles avec LSTM et les modeles ARIMA. |
| </p> |
| <div class="feature-tags"> |
| <span class="badge badge-primary">LSTM</span> |
| <span class="badge badge-secondary">TensorFlow</span> |
| <span class="badge badge-warning">Time Series</span> |
| </div> |
| </div> |
| |
| <div class="feature-card scroll-animate"> |
| <div class="feature-icon">X</div> |
| <h3 class="feature-title">Deploiement</h3> |
| <p class="feature-text"> |
| Apprenez a mettre vos modeles en production avec des API |
| et des interfaces web interactives. |
| </p> |
| <div class="feature-tags"> |
| <span class="badge badge-success">FastAPI</span> |
| <span class="badge badge-primary">Streamlit</span> |
| <span class="badge badge-secondary">Docker</span> |
| </div> |
| </div> |
| </div> |
| </section> |
|
|
| |
| <section class="modules"> |
| <div class="section-header scroll-animate"> |
| <div class="section-eyebrow">Parcours de formation</div> |
| <h2 class="section-title">Accedez aux modules</h2> |
| <p class="section-subtitle"> |
| Trois espaces dedies pour une progression optimale dans votre apprentissage. |
| </p> |
| </div> |
| |
| <div class="modules-grid"> |
| <a href="cours.html" class="module-card scroll-animate"> |
| <div class="module-header"> |
| <div class="module-icon blue">C</div> |
| <div class="module-info"> |
| <div class="module-label">Module 1</div> |
| <h3 class="module-title">Cours Theoriques</h3> |
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| </div> |
| <div class="module-body"> |
| <p class="module-desc"> |
| Contenu theorique complet avec equations mathematiques, |
| explications detaillees et exemples de code Python annote. |
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| <span class="badge badge-primary">Theorie</span> |
| <span class="badge badge-secondary">Mathematiques</span> |
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| <span style="font-size: 0.8rem; color: var(--text-muted);">8 chapitres</span> |
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| <div class="module-icon green">T</div> |
| <div class="module-info"> |
| <div class="module-label">Module 2</div> |
| <h3 class="module-title">Travaux Pratiques</h3> |
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| <div class="module-body"> |
| <p class="module-desc"> |
| 6 notebooks Google Colab guides etape par etape sur des datasets |
| reels de Kaggle. Pret a executer sans installation. |
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| <span style="font-size: 0.8rem; color: var(--text-muted);">6 notebooks</span> |
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| <div class="module-header"> |
| <div class="module-icon orange">F</div> |
| <div class="module-info"> |
| <div class="module-label">Module 3</div> |
| <h3 class="module-title">Questions & Feedback</h3> |
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| <section class="timeline"> |
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| <div class="section-eyebrow">Organisation</div> |
| <h2 class="section-title">Plan de la formation</h2> |
| <p class="section-subtitle"> |
| Un parcours progressif de 8 seances pour maitriser le Machine Learning. |
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| <div class="timeline-dot">1</div> |
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| <h3 class="timeline-title">Introduction au ML & Python</h3> |
| <p class="timeline-text"> |
| Fondamentaux du Machine Learning, environnement Python, |
| NumPy et Pandas pour la manipulation de donnees. |
| </p> |
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| |
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| <div class="timeline-dot">2</div> |
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| <div class="timeline-time"> 2 </div> |
| <h3 class="timeline-title">Pretraitement des Donnees</h3> |
| <p class="timeline-text"> |
| Nettoyage, normalisation, encodage des variables categorielles |
| et feature engineering. |
| </p> |
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| |
| <div class="timeline-item scroll-animate"> |
| <div class="timeline-dot">3</div> |
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| <div class="timeline-time"> 3 </div> |
| <h3 class="timeline-title">Regression Lineaire & Logistique</h3> |
| <p class="timeline-text"> |
| Algorithmes de base, equations mathematiques, |
| implementation avec scikit-learn. |
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| |
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| <div class="timeline-dot">4</div> |
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| <h3 class="timeline-title">Arbres de Decision & Random Forest</h3> |
| <p class="timeline-text"> |
| Classification avec arbres, bagging, boosting et |
| importance des features. |
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| |
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| <div class="timeline-dot">5</div> |
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| <div class="timeline-time"> 5 </div> |
| <h3 class="timeline-title">Reseaux de Neurones avec Keras</h3> |
| <p class="timeline-text"> |
| Introduction au Deep Learning, perceptron multicouche, |
| activation et backpropagation. |
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| |
| <div class="timeline-item scroll-animate"> |
| <div class="timeline-dot">6</div> |
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| <div class="timeline-time"> 6 </div> |
| <h3 class="timeline-title">Series Temporelles & LSTM</h3> |
| <p class="timeline-text"> |
| Prediction de donnees temporelles, reseaux recurrents |
| et modeles LSTM avec TensorFlow. |
| </p> |
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| |
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| <div class="timeline-dot">7</div> |
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| <div class="timeline-time"> 7 </div> |
| <h3 class="timeline-title">Evaluation & Optimisation</h3> |
| <p class="timeline-text"> |
| Metriques de performance, cross-validation, |
| grid search et eviter l'overfitting. |
| </p> |
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| <div class="timeline-dot">8</div> |
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| <div class="timeline-time"> 8 </div> |
| <h3 class="timeline-title">Projet Final & Deploiement</h3> |
| <p class="timeline-text"> |
| Realisation d'un projet complet et mise en production |
| avec une API simple. |
| </p> |
| </div> |
| </div> |
| </div> |
| </section> |
|
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| <section class="cta"> |
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| <h2 class="cta-title">Pret a commencer ?</h2> |
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| Rejoignez des centaines d'etudiants qui ont deja maitrise |
| le Machine Learning avec notre formation. |
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