{"id":505,"date":"2026-03-02T17:16:24","date_gmt":"2026-03-02T17:16:24","guid":{"rendered":"https:\/\/pythonia.fr\/?page_id=505"},"modified":"2026-03-20T09:59:53","modified_gmt":"2026-03-20T09:59:53","slug":"elementor-505","status":"publish","type":"page","link":"https:\/\/pythonia.fr\/index.php\/en\/elementor-505\/","title":{"rendered":"Elementor #505"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"505\" class=\"elementor elementor-505\">\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 with 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\">\n\n  <!-- Left column: Content -->\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      Objectives:\n    <\/h3>\n    <ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n      <li>Understand the fundamental principles of Machine Learning (supervised, unsupervised, reinforcement learning)<\/li>\n      <li>Prepare and transform data for model training using scikit-learn<\/li>\n      <li>Train and evaluate classification, regression, and clustering models<\/li>\n      <li>Discover the basics of Deep Learning using TensorFlow\/Keras<\/li>\n    <\/ul>\n  <\/div>\n\n  <!-- Right column: Links and buttons -->\n  <div style=\"flex:1;min-width:200px;background:#0B1D33;padding:25px;border-radius:12px;height:fit-content\">\n\n    <a href=\"http:\/\/pythonia.fr\/wp-content\/uploads\/2026\/02\/programme_machine_learning.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 Program (PDF)\n    <\/a>\n\n    <a href=\"#\" id=\"btn-dates-en\" style=\"display:block;color:#FFFFFF;font-size:15px;text-decoration:none;padding:12px 0;border-bottom:1px solid #1a3a5c\">\n      \ud83d\udcc5 View dates\n    <\/a>\n\n    <button id=\"btn-contact-en\" 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 Request 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  <!-- Left column: Content -->\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\">Target Audience<\/h3>\n    <ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n      <li>Professionals pursuing an advanced career transition into data science<\/li>\n      <li>Analysts who want to understand the principles and implementation of Machine Learning with Python in order to contribute to ML engineering or predictive analytics projects (real estate, finance).<\/li>\n    <\/ul>\n\n    <h3 style=\"font-size:24px;color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px\">Learning Objectives<\/h3>\n    <ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n      <li>Understand the fundamental principles of Machine Learning (supervised, unsupervised, reinforcement learning)<\/li>\n      <li>Prepare and transform datasets for model training with scikit-learn<\/li>\n      <li>Train and evaluate regression models (linear, polynomial, regularization)<\/li>\n      <li>Train and evaluate classification models (logistic regression, decision trees, Random Forest)<\/li>\n      <li>Apply clustering techniques (K-Means, DBSCAN, hierarchical clustering)<\/li>\n      <li>Discover the basics of Deep Learning with TensorFlow\/Keras<\/li>\n      <li>Build a complete end-to-end ML pipeline using real datasets<\/li>\n    <\/ul>\n\n    <h3 style=\"font-size:24px;color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px\">Prerequisites<\/h3>\n    <ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n      <li>Basic knowledge of Python (variables, functions, loops, data structures)<\/li>\n      <li>Basic mathematical knowledge (descriptive statistics, basic algebra)<\/li>\n      <li>Completion of the Python Beginner training or equivalent level<\/li>\n      <li>A computer with Python 3.x installed and internet access<\/li>\n    <\/ul>\n\n    <h3 style=\"font-size:24px;color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px\">Teaching Methods<\/h3>\n    <ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n      <li>Combination of theory (40%) and practice (60%)<\/li>\n      <li>Interactive lectures and live demonstrations of ML models<\/li>\n      <li>Individual and team labs using scikit-learn, TensorFlow\/Keras and Jupyter Notebook<\/li>\n      <li>Active learning: collaborative problem solving and group analysis of results<\/li>\n      <li>Digital course materials and exercise files (permanent access)<\/li>\n    <\/ul>\n\n    <h3 style=\"font-size:24px;color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px\">Target Certification: RS6763 \u2013 Manipulating, analyzing and visualizing data using Python Data Science modules<\/h3>\n\n    <h1 style=\"font-size:24px;color:#FFFFFF;font-weight:bold;margin-top:40px;margin-bottom:20px\">Detailed Program<\/h1>\n\n    <h3 style=\"font-size:16px;color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px\">DAY 1 \u2014 Machine Learning Fundamentals<\/h3>\n    <ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n      <li>Introduction to ML: definition, types of learning, applications<\/li>\n      <li>Supervised vs unsupervised vs reinforcement learning<\/li>\n      <li>ML pipeline: data \u2192 preparation \u2192 training \u2192 evaluation \u2192 deployment<\/li>\n      <li>Key concepts: features, labels, model, prediction<\/li>\n      <li>Bias and variance: underfitting, overfitting, trade-offs<\/li>\n      <li>Train\/test split: importance, stratification, cross-validation<\/li>\n      <li>Introduction to scikit-learn: installation, conventions, API<\/li>\n    <\/ul>\n\n    <p style=\"font-size:14px;color:#FFFFFF;margin-left:20px;margin-top:15px\">\n      <strong>\u25a0 Hands-on labs:<\/strong><br>\n      \u2192 Lab 1: Exploring a classic dataset (Iris, Titanic)<br>\n      \u2192 Lab 2: First classification model with scikit-learn<br>\n      \u2192 Lab 3: Analyzing the impact of train\/test split on model performance\n    <\/p>\n\n    <h3 style=\"font-size:16px;color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px\">DAY 2 \u2014 Data Preparation and Regression<\/h3>\n    <ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n      <li>Feature engineering: creation, selection, feature importance<\/li>\n      <li>Handling missing values: imputation strategies<\/li>\n      <li>Encoding categorical variables: OneHot, Label, Target encoding<\/li>\n      <li>Normalization and standardization: MinMaxScaler, StandardScaler<\/li>\n      <li>Linear regression: principle, equation, interpretation<\/li>\n      <li>Polynomial regression and regularization (Ridge, Lasso)<\/li>\n      <li>Regression metrics: MSE, RMSE, MAE, R\u00b2<\/li>\n    <\/ul>\n\n    <p style=\"font-size:14px;color:#FFFFFF;margin-left:20px;margin-top:15px\">\n      <strong>\u25a0 Hands-on labs:<\/strong><br>\n      \u2192 Lab 1: Complete data preparation pipeline<br>\n      \u2192 Lab 2: Real estate price prediction (linear regression)<br>\n      \u2192 Lab 3: Comparison of regularization techniques\n    <\/p>\n\n    <h3 style=\"font-size:16px;color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px\">DAY 3 \u2014 Classification and Clustering<\/h3>\n    <ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n      <li>Logistic regression: principle, sigmoid function, decision threshold<\/li>\n      <li>Decision trees: construction, criteria, visualization<\/li>\n      <li>Random Forest: ensemble learning, bagging, feature importance<\/li>\n      <li>Classification metrics: accuracy, precision, recall, F1-score<\/li>\n      <li>Confusion matrix and ROC curve<\/li>\n      <li>K-Means clustering: principle, choosing k, elbow method<\/li>\n      <li>DBSCAN and hierarchical clustering: comparison<\/li>\n    <\/ul>\n\n    <p style=\"font-size:14px;color:#FFFFFF;margin-left:20px;margin-top:15px\">\n      <strong>\u25a0 Hands-on labs:<\/strong><br>\n      \u2192 Lab 1: Spam email classification with Random Forest<br>\n      \u2192 Lab 2: Customer segmentation using K-Means clustering<br>\n      \u2192 Lab 3: Comparing classification models on the same dataset\n    <\/p>\n\n    <h3 style=\"font-size:16px;color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px\">DAY 4 \u2014 Introduction to Deep Learning and Final Project<\/h3>\n    <ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n      <li>Neural networks: perceptron, layers, activation functions<\/li>\n      <li>MLP architecture: dense layers, forward propagation<\/li>\n      <li>Introduction to TensorFlow\/Keras: installation, Sequential API<\/li>\n      <li>Training concepts: epochs, batch size, loss functions, optimizers<\/li>\n      <li>Dimensionality reduction: PCA, t-SNE for visualization<\/li>\n      <li>Transfer learning: concept and use cases<\/li>\n      <li>Saving and loading models<\/li>\n    <\/ul>\n\n    <p style=\"font-size:14px;color:#FFFFFF;margin-left:20px;margin-top:15px\">\n      <strong>\u25a0 Hands-on labs:<\/strong><br>\n      \u2192 Lab 1: MNIST image classification with a neural network<br>\n      \u2192 Lab 2: High-dimensional data visualization with PCA\/t-SNE<br>\n      \u2192 Lab 3 (Final project): Complete end-to-end ML pipeline on real datasets\n    <\/p>\n\n    <h3 style=\"font-size:16px;color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px\">Assessment<\/h3>\n    <ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n      <li>Initial assessment test at the beginning of the training<\/li>\n      <li>Formative assessments: corrected practical exercises, group analysis of results, intermediate quizzes<\/li>\n      <li>Final multiple-choice test of 30 questions (Day 4) \u2014 passing score: 60%<\/li>\n      <li>Certificate of completion issued<\/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 with Python Objectives: Understand the fundamental principles of Machine Learning (supervised, unsupervised, reinforcement learning) Prepare and transform data 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