{"id":7587,"date":"2024-12-03T08:58:17","date_gmt":"2024-12-03T07:58:17","guid":{"rendered":"https:\/\/blog.structuralia.com\/aprendizaje-supervisado-y-no-supervisado"},"modified":"2026-03-27T13:46:28","modified_gmt":"2026-03-27T12:46:28","slug":"aprendizaje-supervisado-y-no-supervisado","status":"publish","type":"post","link":"https:\/\/blog.structuralia.com\/en\/aprendizaje-supervisado-y-no-supervisado","title":{"rendered":"Understanding Supervised and Unsupervised Learning"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_84 counter-hierarchy ez-toc-counter ez-toc-white ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\"><\/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 of Contents\"><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-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/blog.structuralia.com\/en\/aprendizaje-supervisado-y-no-supervisado\/#Dos_tecnicas_empleadas_en_la_ciencia_de_datos\" >Two techniques used in data science<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/blog.structuralia.com\/en\/aprendizaje-supervisado-y-no-supervisado\/#Aprendizaje_supervisado_o_algoritmos_supervisados\" >Supervised learning or supervised algorithms<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/blog.structuralia.com\/en\/aprendizaje-supervisado-y-no-supervisado\/#Aprendizaje_no_supervisado_o_algoritmos_no_supervisados\" >Unsupervised learning or unsupervised algorithms<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/blog.structuralia.com\/en\/aprendizaje-supervisado-y-no-supervisado\/#%C2%BFCuales_son_sus_ventajas\" >What are its advantages?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/blog.structuralia.com\/en\/aprendizaje-supervisado-y-no-supervisado\/#El_futuro_del_machine_learning\" >The Future of Machine Learning<\/a><\/li><\/ul><\/nav><\/div>\n<div id=\"hs_cos_wrapper_post_body\">\n<p style=\"font-size: 14px; text-align: justify;\"><span style=\"color: #666666; font-family: Tahoma, Arial, Helvetica, sans-serif;\">The <span style=\"font-weight: bold;\">supervised and unsupervised learning<\/span> These are two machine learning techniques used to teach computer systems to \u201clearn.\u201d.<\/span><span style=\"color: #ff3b49;\">a<\/span><\/strong><span style=\"color: #1e1e1e;\"> <span style=\"color: #666666;\">and even nutrition. What is the difference between these two techniques of<\/span><\/span><span style=\"color: #666666;\"><strong> machine learning<\/strong>? Where does each one apply? Here is the information.<\/span><\/span><\/p>\n<h2 style=\"font-size: 14px;\"><span class=\"ez-toc-section\" id=\"Dos_tecnicas_empleadas_en_la_ciencia_de_datos\"><\/span><span style=\"color: #b22b3b; font-family: Tahoma, Arial, Helvetica, sans-serif;\"><strong style=\"color: #b22b3b; font-size: 30px;\">Two techniques used in data science<\/strong><\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-family: Tahoma, Arial, Helvetica, sans-serif;\">The <strong>supervised and unsupervised learning<\/strong> are different techniques from<a href=\"https:\/\/blog.structuralia.com\/en\/aplicaciones-inteligencia-artificial\/\"> <span style=\"text-decoration: none; color: #ff3b49; font-style: normal;\"><strong><em>machine learning<\/em><\/strong><\/span><\/a> used to \u201ctrain\u201d computer systems and achieve specific results.<\/span><\/p>\n<h3 style=\"font-size: 20px; padding-left: 40px;\"><span class=\"ez-toc-section\" id=\"Aprendizaje_supervisado_o_algoritmos_supervisados\"><\/span><span style=\"font-family: Tahoma, Arial, Helvetica, sans-serif;\"><strong>Supervised learning or supervised algorithms<\/strong><\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p style=\"padding-left: 40px; text-align: justify;\"><span style=\"font-family: Tahoma, Arial, Helvetica, sans-serif;\">This machine learning technique requires a set of <strong>labeled data<\/strong>, where each sample has a label that includes keywords, descriptions, categories, or attributes. Its purpose is to perform extrapolations and <strong>predict<\/strong> accurate results.<\/span><\/p>\n<p style=\"padding-left: 40px; text-align: justify;\"><span style=\"font-family: Tahoma, Arial, Helvetica, sans-serif;\">The model uses the labeled data to quantify the relevance of the various features in order to gradually improve the model's fit to the known outcome. Thus, the computer system analyzes the provided labels and learns to recognize trends, detect anomalies, or<strong> recognize patterns<\/strong>, as in the following examples:<\/span><\/p>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li aria-level=\"1\">\n<p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, Arial, Helvetica, sans-serif;\">Sort emails (<em>spam<\/em> or not <em>spam<\/em>).<\/span><\/p>\n<\/li>\n<li style=\"text-align: justify;\" aria-level=\"1\"><span style=\"font-family: Tahoma, Arial, Helvetica, sans-serif;\">Detects financial fraud.<\/span><\/li>\n<li style=\"text-align: justify;\" aria-level=\"1\"><span style=\"font-family: Tahoma, Arial, Helvetica, sans-serif;\">Predict the cost of real estate based on its characteristics and location.<\/span><\/li>\n<li style=\"text-align: justify;\" aria-level=\"1\"><span style=\"font-family: Tahoma, Arial, Helvetica, sans-serif;\">It recognizes handwriting and converts it into digital text.<\/span><\/li>\n<li aria-level=\"1\">\n<p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, Arial, Helvetica, sans-serif;\">Predict a company's revenue based on its advertising.<\/span><\/p>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p><span style=\"font-family: Tahoma, Arial, Helvetica, sans-serif;\"><strong><span style=\"font-size: 14px; color: #002e8a;\"><img fetchpriority=\"high\" decoding=\"async\" style=\"margin-left: auto; margin-right: auto; display: block; width: 500px; height: auto; max-width: 100%;\" src=\"https:\/\/blog.structuralia.com\/wp-content\/uploads\/2024\/11\/xhykouALmYlTjVJQANDmpW-vbCbYW8VcFxUUSHSZCQG35CZwfGXrIDmG4GvNix47LIuAUrjdB_l7XMPQWXpWjrNyvYJWVavcWdUp.png\" alt=\"Aprendizaje supervisado o algoritmos supervisados\" width=\"500\" height=\"328\" \/><\/span><\/strong><\/span><\/p>\n<p style=\"font-size: 10px; text-align: center;\"><em>\u00a0<\/em><\/p>\n<h2 style=\"font-size: 14px;\"><span class=\"ez-toc-section\" id=\"Aprendizaje_no_supervisado_o_algoritmos_no_supervisados\"><\/span><span style=\"color: #b22b3b; font-family: Tahoma, Arial, Helvetica, sans-serif;\"><strong style=\"color: #b22b3b; font-size: 30px;\">Unsupervised learning or unsupervised algorithms<\/strong><\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, Arial, Helvetica, sans-serif;\">In this technique of <em>machine learning,<\/em> The information provided to the system is as follows:<strong> data<\/strong> <strong>Unlabeled<\/strong> and unsupervised. In other words, the model must identify patterns and structures in data that lack labels and key annotations. Its goal is to <strong>discover<\/strong> <strong>hidden patterns<\/strong> and related data, and then group them into <em>clusters<\/em> or categories:<\/span><\/p>\n<ul>\n<li style=\"text-align: justify;\" aria-level=\"1\">\n<p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, Arial, Helvetica, sans-serif;\">Categorize different customers based on their purchasing patterns.<\/span><\/p>\n<\/li>\n<li style=\"text-align: justify;\" aria-level=\"1\"><span style=\"font-family: Tahoma, Arial, Helvetica, sans-serif;\">It generates images with less noise and greater visual clarity (dimension reduction).<\/span><\/li>\n<li style=\"text-align: justify;\" aria-level=\"1\"><span style=\"font-family: Tahoma, Arial, Helvetica, sans-serif;\">Identify purchasing patterns in a set of transactions.<\/span><\/li>\n<li style=\"text-align: justify;\" aria-level=\"1\"><span style=\"font-family: Tahoma, Arial, Helvetica, sans-serif;\">Improve cybersecurity.<\/span><\/li>\n<li style=\"text-align: justify;\" aria-level=\"1\"><span style=\"font-family: Tahoma, Arial, Helvetica, sans-serif;\">Create recommendation engines.<\/span><\/li>\n<\/ul>\n<h2 style=\"font-size: 14px; text-align: justify;\"><span class=\"ez-toc-section\" id=\"%C2%BFCuales_son_sus_ventajas\"><\/span><span style=\"color: #b22b3b; font-family: Tahoma, Arial, Helvetica, sans-serif;\"><strong style=\"color: #b22b3b; font-size: 30px;\">What are its advantages?<\/strong><\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, Arial, Helvetica, sans-serif;\">Both types of learning have specific advantages. Within the <strong>supervised algorithm<\/strong> We have:<\/span><\/p>\n<ul>\n<li aria-level=\"1\">\n<p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, Arial, Helvetica, sans-serif;\">High accuracy and reliability. The model learns from accurate, clear examples supervised by humans. It can also continue to improve over time.<\/span><\/p>\n<\/li>\n<li style=\"text-align: justify;\" aria-level=\"1\"><span style=\"font-family: Tahoma, Arial, Helvetica, sans-serif;\">A better understanding of the data. It's possible <strong>sort and categorize<\/strong> data for a clearer analysis, ranging from business data to scientific applications.<\/span><\/li>\n<li aria-level=\"1\">\n<p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, Arial, Helvetica, sans-serif;\">Faster learning. When training with labeled data, the model learns more efficiently.<\/span><\/p>\n<\/li>\n<\/ul>\n<p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, Arial, Helvetica, sans-serif;\">Now, the <strong>unsupervised algorithm<\/strong> offers the following benefits:<\/span><\/p>\n<ul>\n<li aria-level=\"1\">\n<p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, Arial, Helvetica, sans-serif;\">Explore and discover patterns. Identify relationships between variables and hidden patterns.<\/span><\/p>\n<\/li>\n<li style=\"text-align: justify;\" aria-level=\"1\"><span style=\"font-family: Tahoma, Arial, Helvetica, sans-serif;\">Efficient and effective analysis. The following are studied: <strong>huge complexes<\/strong> data.<\/span><\/li>\n<li style=\"text-align: justify;\" aria-level=\"1\"><span style=\"font-family: Tahoma, Arial, Helvetica, sans-serif;\">Lower cost. It does not require the creation and labeling of training data.<\/span><\/li>\n<li aria-level=\"1\">\n<p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, Arial, Helvetica, sans-serif;\">Greater flexibility and adaptability. It can be used with a wide range of data types, such as text, images, audio, and video.<\/span><\/p>\n<\/li>\n<\/ul>\n<h2 style=\"font-size: 14px;\"><span class=\"ez-toc-section\" id=\"El_futuro_del_machine_learning\"><\/span><span style=\"color: #b22b3b; font-family: Tahoma, Arial, Helvetica, sans-serif;\"><strong style=\"color: #b22b3b; font-size: 30px;\">The Future of Machine Learning<\/strong><\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, Arial, Helvetica, sans-serif;\"><span style=\"color: #666666;\">The <em>machine learning<\/em> It is changing our world and promises to further revolutionize various areas of society. With this in mind, the best-prepared professionals are those who, for example, have a<\/span> <span style=\"text-decoration: none; color: #ff3b49;\"><strong>Master's Degree in Artificial Intelligence: Model Management and Implementation<\/strong><\/span>, those who will lead the transformation toward the future.<\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, Arial, Helvetica, sans-serif; color: #666666;\">The <strong>supervised learning<\/strong> It is used when the data tags are known to obtain a <strong>prediction<\/strong>, whereas in the <strong>unsupervised learning<\/strong> is <strong>discover<\/strong> completely new models using unlabeled data. Also:<\/span><\/p>\n<ul>\n<li style=\"text-align: justify;\" aria-level=\"1\">\n<p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, Arial, Helvetica, sans-serif; color: #666666;\">Supervised learning requires <strong>a larger amount of data<\/strong> training than unsupervised learning.<\/span><\/p>\n<\/li>\n<li style=\"text-align: justify;\" aria-level=\"1\">\n<p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, Arial, Helvetica, sans-serif; color: #666666;\">The <strong>accuracy <\/strong>The choice of model in supervised learning depends largely on the <strong>data quality<\/strong>. In unsupervised learning, it depends on the correct choice of the <strong>algorithm and parameters<\/strong>.<\/span><\/p>\n<\/li>\n<\/ul>\n<p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, Arial, Helvetica, sans-serif;\"><a style=\"color: #ffc000; text-decoration: underline;\" href=\"https:\/\/www.structuralia.com\/\"><strong><span style=\"color: #ff3b49;\">Structuralia<\/span><\/strong><\/a> It is a school that offers graduate programs in various areas of artificial intelligence, featuring top professionals and high-quality course content. The future is already in our hands\u2014and so is success.<\/span><\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>El aprendizaje supervisado y no supervisado son dos t\u00e9cnicas de machine learning\u00a0 para hacer \u201caprender\u201d a los sistemas computacionales.a e incluso, alimentaci\u00f3n. \u00bfCu\u00e1l es la diferencia entre estas dos t\u00e9cnicas de aprendizaje autom\u00e1tico? \u00bfD\u00f3nde se aplica cada uno? Esta es la informaci\u00f3n. Dos t\u00e9cnicas empleadas en la ciencia de datos El aprendizaje supervisado y no [&hellip;]<\/p>\n","protected":false},"author":9,"featured_media":4887,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_ayudawp_aiss_exclude":false,"_ayudawp_aiss_summary":"","_ayudawp_aiss_summary_provider":"","_ayudawp_aiss_summary_hash":"","footnotes":""},"categories":[266],"tags":[],"class_list":["post-7587","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-transformacion-digital"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Entendiendo el aprendizaje supervisado y no supervisado<\/title>\n<meta name=\"description\" content=\"El aprendizaje supervisado y no supervisado son dos t\u00e9cnicas de machine learning para hacer \u201caprender\u201d a los sistemas computacionales.\" \/>\n<meta name=\"robots\" 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