{"id":31316,"date":"2025-01-10T10:44:33","date_gmt":"2025-01-10T09:44:33","guid":{"rendered":"https:\/\/www.codemotion.com\/magazine\/?p=31316"},"modified":"2025-01-16T09:48:56","modified_gmt":"2025-01-16T08:48:56","slug":"machine-learning-para-principiantes-iniciar-y-dominar-la-ia","status":"publish","type":"post","link":"https:\/\/www.codemotion.com\/magazine\/es\/inteligencia-artificial\/machine-learning-para-principiantes-iniciar-y-dominar-la-ia\/","title":{"rendered":"Machine Learning para Principiantes: Iniciar y dominar la\u00a0IA"},"content":{"rendered":"\n<p class=\"has-text-align-left\"><strong>Bienvenidos(as) al emocionante mundo del Machine Learning.<\/strong> En esta gu\u00eda, aprender\u00e1s c\u00f3mo iniciar en Machine Learning para principiantes y dominar la Inteligencia Artificial. Ya seamos un principiante curioso, un profesional que busca ampliar sus habilidades o si simplemente est\u00e1s interesado(a) en adentrarte en este campo, est\u00e1s en el lugar correcto. Imagina un mundo donde las m\u00e1quinas aprenden de la experiencia y toman decisiones inteligentes. Suena a ciencia ficci\u00f3n, \u00bfverdad? Pues bien, este mundo ya es una realidad gracias al ML. Ahora, <strong>\u00bfQu\u00e9 es el Machine Learning y por qu\u00e9 est\u00e1 transformando nuestra realidad?<\/strong>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-entendiendo-el-machine-learning\"><strong>Entendiendo el Machine Learning<\/strong><\/h2>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"has-text-align-right\">El machine learning es el campo de estudio que da a los ordenadores la capacidad de aprender sin ser programados de manera expl\u00edcita.\u2014Arthur Samuel, 1959<\/p>\n<\/blockquote>\n<\/blockquote>\n\n\n<div class=\"gb-container gb-container-49241906\">\n<div class=\"gb-container gb-container-9d2ea93b\">\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large\"><img decoding=\"async\" src=\"https:\/\/i.ibb.co\/mGWFbgX\/freepik-candid-image-photography-natural-textures-highly-r-57826.jpg\" alt=\"\"\/><\/figure><\/div>\n<\/div>\n<\/div>\n\n\n<p>Entonces, el ML es un subcampo o rama de la Inteligencia Artificial que se enfoca en desarrollar algoritmos y modelos que pueden aprender de los datos y realizar predicciones o tomar decisiones sin ser expl\u00edcitamente programados. Imagina un mundo donde las m\u00e1quinas pueden aprender de sus experiencias, adaptarse a nuevas situaciones y tomar decisiones inteligentes. Eso es precisamente lo que hace el ML. En otras palabras, iniciar en Machine Learning te permitir\u00e1 comprender c\u00f3mo las m\u00e1quinas aprenden de sus experiencias.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-un-futuro-para-los-desarrolladores\"><strong>Un futuro para los desarrolladores <\/strong><\/h2>\n\n\n\n<p>Los <a href=\"https:\/\/www.codemotion.com\/magazine\/ai-ml\/what-will-the-developer-of-the-future-look-like\">desarrolladores del futuro<\/a> necesitar\u00e1n adaptarse a herramientas y tecnolog\u00edas que les permitan crear soluciones m\u00e1s inteligentes y eficientes. Iniciar en Machine Learning te prepara para estos cambios, y te coloca en una posici\u00f3n privilegiada para liderar la innovaci\u00f3n en la industria tecnol\u00f3gica. <\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-aprender-como-los-nbsp-humanos\"><strong>Aprender como los&nbsp;humanos<\/strong><\/h2>\n\n\n\n<p>A diferencia de las m\u00e1quinas tradicionales que siguen instrucciones estrictas, el Machine Learning permite que los sistemas aprendan de los datos. Esto es fundamental cuando decides iniciar en Machine Learning. Por lo tanto, piensa en c\u00f3mo nosotros, como humanos, aprendemos: observamos, practicamos y mejoramos con el tiempo. De manera similar, las m\u00e1quinas utilizan algoritmos para identificar patrones en grandes cantidades de datos y, a partir de ah\u00ed, hacen predicciones o toman decisiones.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-primeros-pasos-en-machine-learning\"><strong>Primeros pasos en Machine Learning<\/strong><\/h2>\n\n\n\n<p>Pongamos un ejemplo cotidiano, cuando usas una aplicaci\u00f3n de m\u00fasica que te sugiere canciones basadas en tus gustos. Esa aplicaci\u00f3n est\u00e1 utilizando ML para analizar tus preferencias y las de otros usuarios, entonces, cuantas m\u00e1s canciones escuchas, m\u00e1s precisa se vuelve la recomendaci\u00f3n. En resumidas cuentas, es como tener un amigo que conoce tus gustos musicales y siempre te sugiere la canci\u00f3n perfecta.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-paso-1-comprender-los-fundamentos-para-iniciar-en-machine-learning\"><strong><strong>Paso 1: Comprender los Fundamentos para Iniciar en Machine Learning<\/strong><\/strong><\/h3>\n\n\n\n<p>Antes de empezar con Machine Learning, necesitas conocer \u00e1lgebra lineal, estad\u00edstica y c\u00e1lculo. Estas \u00e1reas son esenciales en ML.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"h-fundamentos-matematicos\"><em>Fundamentos matem\u00e1ticos<\/em><\/h4>\n\n\n\n<p>El \u00e1lgebra lineal, la estad\u00edstica y el c\u00e1lculo son fundamentales. Por lo tanto, estas \u00e1reas te ayudar\u00e1n a comprender los <a href=\"https:\/\/iaarbook.github.io\/machine-learning\/#algoritmos-mas-utilizados\">algoritmos de ML<\/a>. Aqu\u00ed hay algunos t\u00e9rminos clave que debemos conocer:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Algoritmo<\/strong>: un conjunto de reglas o instrucciones para resolver un problema.<\/li>\n\n\n\n<li><strong>Modelo<\/strong>: una representaci\u00f3n matem\u00e1tica de un proceso basado en datos.<\/li>\n\n\n\n<li><strong>Entrenamiento<\/strong>: el proceso de ense\u00f1ar a un modelo a partir de datos.<\/li>\n\n\n\n<li><strong>Validaci\u00f3n<\/strong>: evaluar el rendimiento del modelo con datos no vistos durante el entrenamiento.<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"h-aprender-a-programar\"><em>Aprender a programar<\/em><\/h4>\n\n\n\n<p>Saber programar es crucial. Entender un lenguaje de programaci\u00f3n, como Python o R, es importante. Aunque, Python es un buen punto de partida. No necesitas ser un experto, pero s\u00ed entender lo b\u00e1sico. Enf\u00f3cate en comprender la l\u00f3gica y c\u00f3mo se desarrollan programas simples.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-paso-2-explorar-el-campo-del-machine-learning\"><strong><strong><strong>Paso 2: Explorar el campo del Machine Learning <\/strong><\/strong><\/strong><\/h3>\n\n\n\n<p>Una vez que tengas una base en matem\u00e1ticas y programaci\u00f3n, entonces es hora de adentrarte y comenzar en Machine Learning de manera efectiva:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Tipos de Aprendizaje: <\/strong>conocer los diferentes tipos de aprendizaje: supervisado, no supervisado y por refuerzo. Cada uno tiene aplicaciones espec\u00edficas y es importante entender cu\u00e1ndo usar cada uno.<\/li>\n\n\n\n<li><strong>Algoritmos Comunes:<\/strong> aprender sobre algoritmos como regresi\u00f3n lineal, \u00e1rboles de decisi\u00f3n, redes neuronales y m\u00e1quinas de soporte vectorial (SVM). Es importante, comprender c\u00f3mo funcionan, pues te permitir\u00e1 elegir el adecuado para tus proyectos.<\/li>\n\n\n\n<li><strong>Datasets:<\/strong> los conjuntos de datos son fundamentales para el aprendizaje autom\u00e1tico. Puedes usarlos para experimentar con diferentes algoritmos.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-paso-3-practicar-con-proyectos-equenos-de-machine-learning\"><strong>Paso 3: Practicar con proyectos eque\u00f1os de Machine Learning<\/strong><\/h3>\n\n\n\n<p>A medida que aprendes, intenta crear peque\u00f1os proyectos que te permitan aplicar lo que has aprendido. Adem\u00e1s, esto es vital al iniciar en Machine Learning ya que la pr\u00e1ctica refuerza tus habilidades, y te ayuda a mantenerte motivado. <\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><em><strong><strong><strong><em>Ejemplo de C\u00f3digo: Hola Mundo en Python<\/em><\/strong><\/strong><\/strong><\/em><\/li>\n<\/ul>\n\n\n<pre class=\"wp-block-code\" aria-describedby=\"shcb-language-1\" data-shcb-language-name=\"PHP\" data-shcb-language-slug=\"php\"><span><code class=\"hljs language-php\"><span class=\"hljs-keyword\">print<\/span>(<span class=\"hljs-string\">\"\u00a1Hola, Mundo!\"<\/span>)<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-1\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">PHP<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">php<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre>\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large\"><img decoding=\"async\" src=\"https:\/\/i.ibb.co\/0JkB9ff\/freepik-candid-image-photography-natural-textures-highly-r-57808.jpg\" alt=\"\"\/><\/figure><\/div>\n\n\n<h3 class=\"wp-block-heading\" id=\"h-paso-4-familiarizarse-con-las-bibliotecas-de-ml\"><strong>Paso 4: Familiarizarse con las Bibliotecas de ML<\/strong><\/h3>\n\n\n\n<p>Las bibliotecas m\u00e1s utilizadas te ayudar\u00e1n a implementar algoritmos y trabajar con datos al comenzar en Machine Learning:&nbsp;<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>NumPy<\/strong>: ideal para operaciones matem\u00e1ticas y manejo de matrices.<\/li>\n\n\n\n<li><strong>PyTorch:<\/strong> es una biblioteca de c\u00f3digo abierto, importante en el campo del aprendizaje profundo y la inteligencia artificial.<\/li>\n\n\n\n<li><strong>Pandas<\/strong>: excelente para manipulaci\u00f3n y an\u00e1lisis de datos.<\/li>\n\n\n\n<li><strong>Scikit-learn<\/strong>: clave para algoritmos de ML.<\/li>\n\n\n\n<li><strong>TensorFlow y Keras<\/strong>: ideales para redes neuronales y aprendizaje profundo.<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><em><strong>Ejemplo de C\u00f3digo: Uso de NumPy y Pandas<\/strong><\/em><\/li>\n<\/ul>\n\n\n<pre class=\"wp-block-code\" aria-describedby=\"shcb-language-2\" data-shcb-language-name=\"PHP\" data-shcb-language-slug=\"php\"><span><code class=\"hljs language-php\">import numpy <span class=\"hljs-keyword\">as<\/span> np\nimport pandas <span class=\"hljs-keyword\">as<\/span> pd\n\n<span class=\"hljs-keyword\">array<\/span> = np.<span class=\"hljs-keyword\">array<\/span>(&#91;<span class=\"hljs-number\">1<\/span>, <span class=\"hljs-number\">2<\/span>, <span class=\"hljs-number\">3<\/span>, <span class=\"hljs-number\">4<\/span>, <span class=\"hljs-number\">5<\/span>])\n<span class=\"hljs-keyword\">print<\/span>(<span class=\"hljs-string\">\"Array de NumPy:\"<\/span>, <span class=\"hljs-keyword\">array<\/span>)\n\ndata = {<span class=\"hljs-string\">'Nombre'<\/span>: &#91;<span class=\"hljs-string\">'Ana'<\/span>, <span class=\"hljs-string\">'Luis'<\/span>, <span class=\"hljs-string\">'Carlos'<\/span>], <span class=\"hljs-string\">'Edad'<\/span>: &#91;<span class=\"hljs-number\">23<\/span>, <span class=\"hljs-number\">25<\/span>, <span class=\"hljs-number\">22<\/span>]}\ndf = pd.DataFrame(data)\n<span class=\"hljs-keyword\">print<\/span>(<span class=\"hljs-string\">\"DataFrame de Pandas:\\n\"<\/span>, df)<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-2\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">PHP<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">php<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre>\n\n\n<p>Ahora s\u00ed, vamos a desglosar este c\u00f3digo:<\/p>\n\n\n\n<h6 class=\"wp-block-heading\" id=\"h-importamos-bibliotecas-de-python-en-este-ejemplo-en-particular-numpy-y-pandas-numpy-se-utiliza-para-operaciones-matematicas-y-manejo-de-matrices-mientras-que-pandas-se-usa-para-manipulacion-y-analisis-de-datos\"><em><strong>Importamos Bibliotecas de Python:<\/strong><\/em> en este ejemplo en particular,<strong> <\/strong><code>NumPy<\/code> y <code>Pandas<\/code>. <code>NumPy<\/code> se utiliza para operaciones matem\u00e1ticas y manejo de matrices, mientras que <code>Pandas<\/code> se usa para manipulaci\u00f3n y an\u00e1lisis de datos.<\/h6>\n\n\n<pre class=\"wp-block-code\" aria-describedby=\"shcb-language-3\" data-shcb-language-name=\"JavaScript\" data-shcb-language-slug=\"javascript\"><span><code class=\"hljs language-javascript\"><span class=\"hljs-keyword\">import<\/span> numpy <span class=\"hljs-keyword\">as<\/span> np\n<span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-3\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">JavaScript<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">javascript<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre>\n\n\n<h6 class=\"wp-block-heading\" id=\"h-crear-un-array-de-numpy-en-esta-parte-estamos-creando-un-array-arreglo-de-numpy-con-los-numeros-del-1-al-5-luego-imprimimos-este-array\"><em><strong>Crear un Array de NumPy:<\/strong><\/em> en esta parte, estamos creando un array (arreglo) de <code>NumPy<\/code> con los n\u00fameros del 1 al 5. Luego, imprimimos este array.<\/h6>\n\n\n<pre class=\"wp-block-code\" aria-describedby=\"shcb-language-4\" data-shcb-language-name=\"PHP\" data-shcb-language-slug=\"php\"><span><code class=\"hljs language-php\"><span class=\"hljs-keyword\">array<\/span> = np.<span class=\"hljs-keyword\">array<\/span>(&#91;<span class=\"hljs-number\">1<\/span>, <span class=\"hljs-number\">2<\/span>, <span class=\"hljs-number\">3<\/span>, <span class=\"hljs-number\">4<\/span>, <span class=\"hljs-number\">5<\/span>])\n<span class=\"hljs-keyword\">print<\/span>(<span class=\"hljs-string\">\"Array de NumPy:\"<\/span>, <span class=\"hljs-keyword\">array<\/span>)<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-4\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">PHP<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">php<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre>\n\n\n<p>El resultado ser\u00e1:<\/p>\n\n\n<pre class=\"wp-block-code\" aria-describedby=\"shcb-language-5\" data-shcb-language-name=\"JavaScript\" data-shcb-language-slug=\"javascript\"><span><code class=\"hljs language-javascript\"><span class=\"hljs-built_in\">Array<\/span> de NumPy: &#91;<span class=\"hljs-number\">1<\/span> <span class=\"hljs-number\">2<\/span> <span class=\"hljs-number\">3<\/span> <span class=\"hljs-number\">4<\/span> <span class=\"hljs-number\">5<\/span>]<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-5\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">JavaScript<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">javascript<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre>\n\n\n<h6 class=\"wp-block-heading\" id=\"h-crear-un-dataframe-de-pandas-ahora-creamos-un-diccionario-llamado-data-con-dos-claves-nombre-y-edad-cada-clave-tiene-una-lista-de-valores-luego-usamos-este-diccionario-para-crear-un-dataframe-de-pandas-que-es-una-estructura-de-datos-similar-a-una-tabla-en-una-base-de-datos-o-una-hoja-de-calculo-finalmente-imprimimos-el-dataframe\"><em><strong>Crear un DataFrame de Pandas:<\/strong><\/em> ahora, creamos un diccionario llamado <code>data<\/code> con dos claves: <code>Nombre<\/code> y <code>Edad<\/code>. Cada clave tiene una lista de valores. Luego, usamos este diccionario para crear un <code>DataFrame<\/code> de <code>Pandas<\/code>, que es una estructura de datos similar a una tabla en una base de datos o una hoja de c\u00e1lculo. Finalmente, imprimimos el <code>DataFrame<\/code>.<\/h6>\n\n\n<pre class=\"wp-block-code\" aria-describedby=\"shcb-language-6\" data-shcb-language-name=\"PHP\" data-shcb-language-slug=\"php\"><span><code class=\"hljs language-php\">data = {<span class=\"hljs-string\">'Nombre'<\/span>: &#91;<span class=\"hljs-string\">'Ana'<\/span>, <span class=\"hljs-string\">'Luis'<\/span>, <span class=\"hljs-string\">'Carlos'<\/span>], <span class=\"hljs-string\">'Edad'<\/span>: &#91;<span class=\"hljs-number\">23<\/span>, <span class=\"hljs-number\">25<\/span>, <span class=\"hljs-number\">22<\/span>]}\ndf = pd.DataFrame(data)\n<span class=\"hljs-keyword\">print<\/span>(<span class=\"hljs-string\">\"DataFrame de Pandas:\\n\"<\/span>, df)<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-6\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">PHP<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">php<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre>\n\n\n<p>El resultado ser\u00e1:<\/p>\n\n\n<pre class=\"wp-block-code\"><span><code class=\"hljs\">DataFrame de Pandas:\n   Nombre  Edad\n0     Ana    23\n1    Luis    25\n2  Carlos    22<\/code><\/span><\/pre>\n\n\n<p>Para resumir, este c\u00f3digo nos muestra c\u00f3mo crear y manipular datos utilizando <code>NumPy<\/code> y <code>Pandas<\/code>, dos herramientas fundamentales en el an\u00e1lisis de datos con Python.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-paso-5-realizar-proyectos-practicos-en-machine-learning\"><strong>Paso 5. Realizar Proyectos Pr\u00e1cticos en Machine Learning<\/strong><\/h3>\n\n\n\n<p>Aplica tus conocimientos desarrollando proyectos peque\u00f1os y desafiantes. Esto te ayudar\u00e1 a consolidar tus habilidades y ganar experiencia pr\u00e1ctica. La mejor manera de aprender cuando Inicias en Machine Learning es haciendo. Puedes comenzar con un proyecto simple, como la predicci\u00f3n de precios de viviendas o la clasificaci\u00f3n de flores. Aqu\u00ed te presento algunas ideas para proyectos iniciales:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Clasificaci\u00f3n de im\u00e1genes:<\/strong> usa un conjunto de datos como MNIST para crear un modelo que clasifique d\u00edgitos escritos a mano.<\/li>\n\n\n\n<li><strong>An\u00e1lisis de sentimientos:<\/strong> desarrolla un modelo que analice comentarios o rese\u00f1as para determinar su tono (positivo o negativo).<\/li>\n\n\n\n<li><strong>Predicci\u00f3n de precios:<\/strong> utiliza datos hist\u00f3ricos para predecir precios futuros en un conjunto de datos financiero<\/li>\n\n\n\n<li><strong><em>Ejemplo de C\u00f3digo: Clasificaci\u00f3n de Flores con Scikit-learn<\/em><\/strong><\/li>\n<\/ul>\n\n\n<pre class=\"wp-block-code\" aria-describedby=\"shcb-language-7\" data-shcb-language-name=\"JavaScript\" data-shcb-language-slug=\"javascript\"><span><code class=\"hljs language-javascript\"><span class=\"hljs-keyword\">from<\/span> sklearn.datasets <span class=\"hljs-keyword\">import<\/span> load_iris\n<span class=\"hljs-keyword\">from<\/span> sklearn.model_selection <span class=\"hljs-keyword\">import<\/span> train_test_split\n<span class=\"hljs-keyword\">from<\/span> sklearn.ensemble <span class=\"hljs-keyword\">import<\/span> RandomForestClassifier\n<span class=\"hljs-keyword\">from<\/span> sklearn.metrics <span class=\"hljs-keyword\">import<\/span> accuracy_score\n\niris = load_iris()\nX = iris.data\ny = iris.target\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=<span class=\"hljs-number\">0.2<\/span>, random_state=<span class=\"hljs-number\">42<\/span>)\n\nmodel = RandomForestClassifier(n_estimators=<span class=\"hljs-number\">100<\/span>)\nmodel.fit(X_train, y_train)\n\ny_pred = model.predict(X_test)\naccuracy = accuracy_score(y_test, y_pred)\nprint(<span class=\"hljs-string\">\"Precisi\u00f3n del modelo:\"<\/span>, accuracy)<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-7\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">JavaScript<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">javascript<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre>\n\n\n<p>\u00bfQu\u00e9 hace este c\u00f3digo? Paso a paso:<\/p>\n\n\n\n<h6 class=\"wp-block-heading\" id=\"h-importar-bibliotecas-estas-bibliotecas-nos-permiten-cargar-conjuntos-de-datos-dividirlos-en-conjuntos-de-entrenamiento-y-prueba-entrenar-un-modelo-de-random-forest-y-evaluar-su-precision\"><em><em><strong>Importar Bibliotecas: <\/strong><\/em><\/em> estas bibliotecas nos permiten cargar conjuntos de datos, dividirlos en conjuntos de entrenamiento y prueba, entrenar un modelo de Random Forest y evaluar su precisi\u00f3n.<\/h6>\n\n\n<pre class=\"wp-block-code\" aria-describedby=\"shcb-language-8\" data-shcb-language-name=\"JavaScript\" data-shcb-language-slug=\"javascript\"><span><code class=\"hljs language-javascript\"><span class=\"hljs-keyword\">from<\/span> sklearn.datasets <span class=\"hljs-keyword\">import<\/span> load_iris\n<span class=\"hljs-keyword\">from<\/span> sklearn.model_selection <span class=\"hljs-keyword\">import<\/span> train_test_split\n<span class=\"hljs-keyword\">from<\/span> sklearn.ensemble <span class=\"hljs-keyword\">import<\/span> RandomForestClassifier\n<span class=\"hljs-keyword\">from<\/span> sklearn.metrics <span class=\"hljs-keyword\">import<\/span> accuracy_score<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-8\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">JavaScript<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">javascript<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre>\n\n\n<h6 class=\"wp-block-heading\" id=\"h-cargar-el-conjunto-de-datos-iris-luego-cargamos-el-conjunto-de-datos-iris-que-es-un-conjunto-de-datos-clasico-en-ml-que-contiene-informacion-sobre-diferentes-tipos-de-flores-iris-x-contiene-las-caracteristicas-como-la-longitud-y-el-ancho-de-los-petalos-ademas-y-nbsp-contiene-las-etiquetas-las-especies-de-las-flores\"><em><em><em><strong>Cargar el Conjunto de Datos Iris:<\/strong><\/em><\/em><\/em> luego, cargamos el conjunto de datos Iris, que es un conjunto de datos cl\u00e1sico en ML que contiene informaci\u00f3n sobre diferentes tipos de flores Iris. <code>X<\/code> contiene las caracter\u00edsticas (como la longitud y el ancho de los p\u00e9talos), adem\u00e1s,  <code>y<\/code>&nbsp;contiene las etiquetas (las especies de las flores).<\/h6>\n\n\n<pre class=\"wp-block-code\"><span><code class=\"hljs\">iris = load_iris()\nX = iris.data\ny = iris.target<\/code><\/span><\/pre>\n\n\n<h6 class=\"wp-block-heading\" id=\"h-dividir-los-datos-en-conjuntos-de-entrenamiento-y-prueba-ahora-dividimos-los-datos-en-dos-conjuntos-uno-para-entrenamiento-x-train-y-y-train-y-otro-para-prueba-x-test-y-y-test-en-este-caso-practico-el-parametro-test-size-0-2-indica-que-el-20-de-los-datos-se-utilizaran-para-la-prueba-y-el-80-restante-para-el-entrenamiento-y-random-state-42-asegura-que-la-division-sea-reproducible\"><em><em><em><em><strong>Dividir los Datos en Conjuntos de Entrenamiento y Prueba:<\/strong> <\/em><\/em><\/em><\/em> ahora dividimos los datos en dos conjuntos: uno para entrenamiento (<code>X_train<\/code> y <code>y_train<\/code>) y otro para prueba (<code>X_test<\/code> y <code>y_test<\/code>). En \u00e9ste caso pr\u00e1ctico, el par\u00e1metro <code>test_size=0.2<\/code> indica que el 20% de los datos se utilizar\u00e1n para la prueba, y el 80% restante para el entrenamiento y<code>random_state=42<\/code> asegura que la divisi\u00f3n sea reproducible.<\/h6>\n\n\n<pre class=\"wp-block-code\"><span><code class=\"hljs\">X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)<\/code><\/span><\/pre>\n\n\n<h6 class=\"wp-block-heading\" id=\"h-entrenar-un-modelo-de-random-forest-aqui-estamos-creando-un-modelo-de-random-forest-con-100-arboles-n-estimators-100-luego-entrenamos-el-modelo-utilizando-los-datos-de-entrenamiento-x-train-y-y-train\"><em><em><em><em><strong>Entrenar un Modelo de Random Forest:<\/strong><\/em><\/em><\/em><\/em> aqu\u00ed, estamos creando un modelo de Random Forest con 100 \u00e1rboles (<code>n_estimators=100<\/code>). Luego, entrenamos el modelo utilizando los datos de entrenamiento (<code>X_train<\/code> y <code>y_train<\/code>).<\/h6>\n\n\n<pre class=\"wp-block-code\"><span><code class=\"hljs\">model = RandomForestClassifier(n_estimators=100)\nmodel.fit(X_train, y_train)<\/code><\/span><\/pre>\n\n\n<h6 class=\"wp-block-heading\" id=\"h-hacer-predicciones-y-evaluar-el-modelo-finalmente-utilizamos-el-modelo-entrenado-para-hacer-predicciones-sobre-los-datos-de-prueba-x-test-luego-calculamos-la-precision-del-modelo-comparando-las-predicciones-y-pred-con-las-etiquetas-reales-y-test-utilizando-la-funcion-accuracy-score-por-ultimo-la-precision-se-imprime-en-la-consola\"><em><em><em><em><em><strong>Hacer Predicciones y Evaluar el Modelo:<\/strong><\/em><\/em><\/em><\/em><\/em> finalmente, utilizamos el modelo entrenado para hacer predicciones sobre los datos de prueba (<code>X_test<\/code>). Luego, calculamos la precisi\u00f3n del modelo comparando las predicciones (<code>y_pred<\/code>) con las etiquetas reales (<code>y_test<\/code>) utilizando la funci\u00f3n <code>accuracy_score<\/code>. Por \u00faltimo, la precisi\u00f3n se imprime en la consola.<\/h6>\n\n\n<pre class=\"wp-block-code\" aria-describedby=\"shcb-language-9\" data-shcb-language-name=\"PHP\" data-shcb-language-slug=\"php\"><span><code class=\"hljs language-php\">y_pred = model.predict(X_test)\naccuracy = accuracy_score(y_test, y_pred)\n<span class=\"hljs-keyword\">print<\/span>(<span class=\"hljs-string\">\"Precisi\u00f3n del modelo:\"<\/span>, accuracy)<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-9\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">PHP<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">php<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre>\n\n\n<p>En resumen, este c\u00f3digo carga el conjunto de datos Iris, lo divide en conjuntos de entrenamiento y prueba, entrena un modelo de Random Forest y eval\u00faa su precisi\u00f3n. Este es un ejemplo b\u00e1sico de c\u00f3mo se puede aplicar ML para la clasificaci\u00f3n de datos.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-paso-6-explorar-proyectos-mas-avanzados-en-ml\"><strong>Paso 6: Explorar Proyectos M\u00e1s Avanzados en ML<\/strong><\/h3>\n\n\n\n<p>Ahora s\u00ed, una vez que te sientas c\u00f3modo con los conceptos b\u00e1sicos, puedes explorar proyectos m\u00e1s avanzados en el campo del Machine Learning, como el procesamiento de lenguaje natural (NLP) o la visi\u00f3n por computadora.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large\"><img decoding=\"async\" src=\"https:\/\/i.ibb.co\/HnGHbcs\/3-D-professional-image-for-a-technology-magazine-article-on-starting-with-Machine-Learning-from-scra.jpg\" alt=\"\"\/><\/figure><\/div>\n\n\n<h2 class=\"wp-block-heading\" id=\"h-beneficios-de-iniciar-en-machine-learning\"><strong>Beneficios de Iniciar en Machine Learning<\/strong><\/h2>\n\n\n\n<p>El Machine Learning no solo es fascinante desde un punto de vista t\u00e9cnico; adem\u00e1s, tambi\u00e9n tiene un impacto real en nuestras vidas. Por ejemplo, desde personalizar anuncios en l\u00ednea y mejorar diagn\u00f3sticos m\u00e9dicos, esta tecnolog\u00eda est\u00e1 cambiando industrias enteras y haciendo nuestras vidas m\u00e1s f\u00e1ciles y eficientes. Algunas de las aplicaciones m\u00e1s comunes incluyen:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Reconocimiento de im\u00e1genes:<\/strong> identificar objetos, personas o lugares en im\u00e1genes y videos.<\/li>\n\n\n\n<li><strong>Procesamiento de lenguaje natural:<\/strong> comprender y generar lenguaje humano.<\/li>\n\n\n\n<li><strong>Recomendaciones personalizadas:<\/strong> sugerir productos, m\u00fasica o contenido basado en tus preferencias.<\/li>\n\n\n\n<li><strong>Detecci\u00f3n de fraudes:<\/strong> identificar transacciones sospechosas y prevenir el fraude financiero.<\/li>\n\n\n\n<li><strong>Predicci\u00f3n de tendencias:<\/strong> predecir el comportamiento del mercado, el clima o la demanda de productos.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-el-impacto-del-machine-learning-en-nuestro-futuro\"><strong>El Impacto del Machine Learning en Nuestro Futuro<\/strong><\/h2>\n\n\n\n<p>A medida que avanzamos hacia el futuro, el Machine Learning seguir\u00e1 desempe\u00f1ando un papel crucial en la forma en que interactuamos con la tecnolog\u00eda. Aprender sobre ML te coloca en la vanguardia de una revoluci\u00f3n que est\u00e1 cambiando el mundo. En otras palabras, el Machine Learning es como darle a las m\u00e1quinas la capacidad de aprender y crecer, tal como lo hacemos nosotros. Es una herramienta poderosa que est\u00e1 moldeando <a href=\"https:\/\/www.codemotion.com\/magazine\/es\/ciencia-de-datos\/descifrando-roles-datos\/\">roles<\/a>, nuestro presente y futuro.&nbsp;<\/p>\n\n\n\n<p>El Machine Learning es un campo en constante evoluci\u00f3n, y aunque iniciar en ML puede parecer desafiante y abrumador, con una hoja de ruta clara, paciencia, pr\u00e1ctica y la dedicaci\u00f3n adecuada, puedes dominar esta disciplina. As\u00ed, adquirir\u00e1s las habilidades necesarias para dominar esta tecnolog\u00eda y contribuir al futuro de la Inteligencia Artificial. \u00a1Descubre las posibilidades infinitas que el Machine Learning tiene para ofrecer!<\/p>\n\n\n\n<ul class=\"wp-block-social-links is-style-default is-content-justification-right is-layout-flex wp-container-core-social-links-is-layout-765c4724 wp-block-social-links-is-layout-flex\"><li class=\"wp-social-link wp-social-link-linkedin  wp-block-social-link\"><a rel=\"noopener nofollow\" target=\"_blank\" href=\"https:\/\/www.linkedin.com\/in\/orlibetdungonzalez\" class=\"wp-block-social-link-anchor\"><svg width=\"24\" height=\"24\" viewBox=\"0 0 24 24\" version=\"1.1\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M19.7,3H4.3C3.582,3,3,3.582,3,4.3v15.4C3,20.418,3.582,21,4.3,21h15.4c0.718,0,1.3-0.582,1.3-1.3V4.3 C21,3.582,20.418,3,19.7,3z M8.339,18.338H5.667v-8.59h2.672V18.338z M7.004,8.574c-0.857,0-1.549-0.694-1.549-1.548 c0-0.855,0.691-1.548,1.549-1.548c0.854,0,1.547,0.694,1.547,1.548C8.551,7.881,7.858,8.574,7.004,8.574z M18.339,18.338h-2.669 v-4.177c0-0.996-0.017-2.278-1.387-2.278c-1.389,0-1.601,1.086-1.601,2.206v4.249h-2.667v-8.59h2.559v1.174h0.037 c0.356-0.675,1.227-1.387,2.526-1.387c2.703,0,3.203,1.779,3.203,4.092V18.338z\"><\/path><\/svg><span class=\"wp-block-social-link-label screen-reader-text\">LinkedIn<\/span><\/a><\/li>\n\n<li class=\"wp-social-link wp-social-link-github  wp-block-social-link\"><a rel=\"noopener nofollow\" target=\"_blank\" href=\"https:\/\/github.com\/orliluq\" class=\"wp-block-social-link-anchor\"><svg width=\"24\" height=\"24\" viewBox=\"0 0 24 24\" version=\"1.1\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M12,2C6.477,2,2,6.477,2,12c0,4.419,2.865,8.166,6.839,9.489c0.5,0.09,0.682-0.218,0.682-0.484 c0-0.236-0.009-0.866-0.014-1.699c-2.782,0.602-3.369-1.34-3.369-1.34c-0.455-1.157-1.11-1.465-1.11-1.465 c-0.909-0.62,0.069-0.608,0.069-0.608c1.004,0.071,1.532,1.03,1.532,1.03c0.891,1.529,2.341,1.089,2.91,0.833 c0.091-0.647,0.349-1.086,0.635-1.337c-2.22-0.251-4.555-1.111-4.555-4.943c0-1.091,0.39-1.984,1.03-2.682 C6.546,8.54,6.202,7.524,6.746,6.148c0,0,0.84-0.269,2.75,1.025C10.295,6.95,11.15,6.84,12,6.836 c0.85,0.004,1.705,0.114,2.504,0.336c1.909-1.294,2.748-1.025,2.748-1.025c0.546,1.376,0.202,2.394,0.1,2.646 c0.64,0.699,1.026,1.591,1.026,2.682c0,3.841-2.337,4.687-4.565,4.935c0.359,0.307,0.679,0.917,0.679,1.852 c0,1.335-0.012,2.415-0.012,2.741c0,0.269,0.18,0.579,0.688,0.481C19.138,20.161,22,16.416,22,12C22,6.477,17.523,2,12,2z\"><\/path><\/svg><span class=\"wp-block-social-link-label screen-reader-text\">GitHub<\/span><\/a><\/li>\n\n<li class=\"wp-social-link wp-social-link-medium  wp-block-social-link\"><a rel=\"noopener nofollow\" target=\"_blank\" href=\"https:\/\/medium.com\/@orlidev\" class=\"wp-block-social-link-anchor\"><svg width=\"24\" height=\"24\" viewBox=\"0 0 24 24\" version=\"1.1\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M13.2,12c0,3-2.4,5.4-5.3,5.4S2.6,15,2.6,12s2.4-5.4,5.3-5.4S13.2,9,13.2,12 M19.1,12c0,2.8-1.2,5-2.7,5s-2.7-2.3-2.7-5s1.2-5,2.7-5C17.9,7,19.1,9.2,19.1,12 M21.4,12c0,2.5-0.4,4.5-0.9,4.5c-0.5,0-0.9-2-0.9-4.5s0.4-4.5,0.9-4.5C21,7.5,21.4,9.5,21.4,12\"><\/path><\/svg><span class=\"wp-block-social-link-label screen-reader-text\">Medium<\/span><\/a><\/li>\n\n<li class=\"wp-social-link wp-social-link-chain  wp-block-social-link\"><a rel=\"noopener nofollow\" target=\"_blank\" href=\"https:\/\/linktr.ee\/orlidevs\" class=\"wp-block-social-link-anchor\"><svg width=\"24\" height=\"24\" viewBox=\"0 0 24 24\" version=\"1.1\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M15.6,7.2H14v1.5h1.6c2,0,3.7,1.7,3.7,3.7s-1.7,3.7-3.7,3.7H14v1.5h1.6c2.8,0,5.2-2.3,5.2-5.2,0-2.9-2.3-5.2-5.2-5.2zM4.7,12.4c0-2,1.7-3.7,3.7-3.7H10V7.2H8.4c-2.9,0-5.2,2.3-5.2,5.2,0,2.9,2.3,5.2,5.2,5.2H10v-1.5H8.4c-2,0-3.7-1.7-3.7-3.7zm4.6.9h5.3v-1.5H9.3v1.5z\"><\/path><\/svg><span class=\"wp-block-social-link-label screen-reader-text\">Link<\/span><\/a><\/li><\/ul>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Bienvenidos(as) al emocionante mundo del Machine Learning. En esta gu\u00eda, aprender\u00e1s c\u00f3mo iniciar en Machine Learning para principiantes y dominar la Inteligencia Artificial. Ya seamos un principiante curioso, un profesional que busca ampliar sus habilidades o si simplemente est\u00e1s interesado(a) en adentrarte en este campo, est\u00e1s en el lugar correcto. Imagina un mundo donde las&#8230; <a class=\"more-link\" href=\"https:\/\/www.codemotion.com\/magazine\/es\/inteligencia-artificial\/machine-learning-para-principiantes-iniciar-y-dominar-la-ia\/\">Read more<\/a><\/p>\n","protected":false},"author":313,"featured_media":20963,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_editorskit_title_hidden":false,"_editorskit_reading_time":0,"_editorskit_is_block_options_detached":false,"_editorskit_block_options_position":"{}","_uag_custom_page_level_css":"","_genesis_hide_title":false,"_genesis_hide_breadcrumbs":false,"_genesis_hide_singular_image":false,"_genesis_hide_footer_widgets":false,"_genesis_custom_body_class":"","_genesis_custom_post_class":"","_genesis_layout":"","footnotes":""},"categories":[10610,10598],"tags":[12919,10682,10664,12922,10786],"collections":[12717,11388,12719],"class_list":{"0":"post-31316","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-aprendizaje-automatico","8":"category-inteligencia-artificial","9":"tag-artificial-intelligence-es","10":"tag-consejos-de-carrera","11":"tag-ia","12":"tag-machine-learning-es","13":"tag-python-es","14":"collections-ai","15":"collections-codemotion-guides","16":"collections-ia","17":"entry"},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v26.9 (Yoast SEO v26.9) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Machine Learning para Principiantes: Iniciar y dominar la\u00a0IA - Codemotion Magazine Inteligencia Artificial<\/title>\n<meta name=\"description\" content=\"Descubre c\u00f3mo iniciar en Machine Learning con esta gu\u00eda. 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