{"id":2877,"date":"2021-08-07T13:12:53","date_gmt":"2021-08-07T16:12:53","guid":{"rendered":"https:\/\/waisdata.com\/?post_type=blog-wais&p=2877"},"modified":"2021-09-03T12:37:30","modified_gmt":"2021-09-03T15:37:30","slug":"machine-learning-explicado-para-todos","status":"publish","type":"blog-wais","link":"https:\/\/waisdata.com\/en\/blog-wais\/machine-learning-explicado-para-todos\/","title":{"rendered":"Machine Learning explained for everyone"},"content":{"rendered":"
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Machine Learning explained for everyone<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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2021-08-07<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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In this new post we are going to explain in a simple way what Machine Learning is, what is this term that is very fashionable and more and more present nowadays.<\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\"If you can't explain it simply, you haven't understood it well\" - Albert Einstein<\/strong><\/center><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Motivation<\/h3><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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When it comes to explaining a concept, telling how a certain technology works or selling a service, it is extremely important to leave technical language aside and convey our ideas in a simple way. We could say that it is a friendly, and why not inclusive, way for people with little or no knowledge to understand what we do. In turn, this facilitates word of mouth, so having the ability to simplify complex concepts is key. That is exactly what we will try to do in this blog when talking about Machine Learning.<\/span><\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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What is meant by using Machine Learning?<\/h3><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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It is when we load a certain amount of data into a computer program with the purpose that the computer, based on a series of operations, is able to make predictions or obtain desired results in an automated and intelligent way.<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Why do we talk about \" learning\"?<\/h3><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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In a simple way, the computer is able to \"learn\" from data provided by the user in order to achieve a specific goal. The way the computer achieves this learning is through algorithms. Algorithms are a series of defined steps that are programmed in a certain language that the computer will be able to interpret and execute. An algorithm can range from a simple equation of a straight line, to complex systems involving statistical methods, mathematical rules and logical expressions. The purpose of implementing these algorithms is to give the computer the ability to interpret the data provided to it. Behind this is the intention of obtaining valuable results, being able to find patterns hidden to the human eye, and even process a larger amount of data than one would be capable of.<\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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What types of Machine Learning are there?<\/h3><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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The problem to be solved\u00a0will define the environment in which the solution will be developed and the factors that will affect decision making.\u00a0<\/span>\u00a0<\/span>Because of this, we can find different types of machine learning:<\/span>\u00a0supervised, unsupervised and reinforcement learning.<\/span><\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Supervised machine learning<\/h3><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Here the data with which we build the model are \"labeled\". This means that the outcome of the variable we want to predict (dependent or target variable) is known for certain observations. For example, if we want to train a model that predicts whether a customer will cancel his subscription, we will need a dataset with a variable containing the outcome of the cancellation (cancelled or not cancelled) for previous or existing customers. This outcome has to be labeled by someone before training a model. If this dataset contains 5,000 observations, it is necessary that all of them have the labeled outcome. The goal of the model is to learn the relationship between this outcome column and the other characteristics (also called independent variables or predictor variables).<\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Unsupervised machine learning<\/h3><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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It is the opposite of supervised machine learning. Our data are not labeled and these labels are not necessary to build the model. Within this type of algorithm, clustering is the most commonly used. Clustering is very popular for customer segmentation. Here the algorithm seeks to group customers with similar behaviors and\/or characteristics from the data provided.<\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Reinforcement learning<\/h3><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Los algoritmos de aprendizaje por refuerzo se distinguen a\u00fan m\u00e1s que los dos anteriores. Aqu\u00ed se definen modelos y funciones enfocadas en maximizar una medida de \u201crecompensas\u201d, basados en \u201cacciones\u201d y el ambiente en donde se desempe\u00f1ar\u00e1 el sistema. Este algoritmo es el m\u00e1s relacionado a la psicolog\u00eda conductista de los humanos. Es un modelo acci\u00f3n-recompensa, en d\u00f3nde el algoritmo busca la mejor \u201crecompensa\u201d dada por el ambiente, estando sus acciones sujetas a estas recompensas. Este tipo de m\u00e9todos pueden usarse para hacer que los robots aprendan a realizar diferentes tareas c\u00f3mo por ejemplo: jugar al ajedrez. Aqu\u00ed el robot aprende jugada tras jugada a partir de la \u201crecompensa\u201d obtenida en cada una de ellas. Esta recompensa se define de acuerdo al objetivo del sistema. En el caso del robot, la recompensa podr\u00e1 ser la probabilidad de ganar la partida.<\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Last words<\/h3><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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It only remains to say that the approach chosen will depend on what you want to achieve. Each type of machine learning has its advantages and areas of opportunity. The challenge lies in knowing how to determine the nature of our problem. In this way, we will be able to determine which type of Machine Learning is the most convenient to implement and, in this way, succeed in solving our problem.<\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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We invite you to continue discovering the different fields of application where Data Science is used Data Science<\/span>\u00a0on our website by clicking on the following link<\/span><\/a><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Published by<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Wais<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"

Machine Learning is gaining more and more strength nowadays. In this publication we explain in a friendly way what it is and what are the different types of Machine Learning that exist.<\/p>","protected":false},"author":13,"featured_media":2896,"comment_status":"open","ping_status":"closed","template":"","meta":{"site-sidebar-layout":"no-sidebar","site-content-layout":"page-builder","ast-global-header-display":"","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":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":""},"categories":[12,1],"tags":[],"acf":[],"yoast_head":"\n#WaisBlog: Machine Learning explicado para todos | Ciencia de datos<\/title>\n<meta name=\"description\" content=\"El Machine Learning o Aprendizaje Autom\u00e1tica toma cada vez m\u00e1s fuerza en la actualidad. Esto demanda poder explicarlo de forma amigable.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/waisdata.com\/en\/blog-wais\/machine-learning-explicado-para-todos\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"#WaisBlog: Machine Learning explicado para todos | Ciencia de datos\" \/>\n<meta property=\"og:description\" content=\"El Machine Learning o Aprendizaje Autom\u00e1tica toma cada vez m\u00e1s fuerza en la actualidad. 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