But how can data help us make informed decisions quickly and efficiently?<\/strong><\/p>\n\n\n\n This is precisely where Machine Learning comes in.<\/p>\n\n\n\n In simple terms, Machine Learning is a field of artificial intelligence that enables computers to learn from data. Instead of being programmed with fixed rules, Machine Learning algorithms analyze data, identify patterns, and evolve based on experience.<\/p>\n\n\n\n What can computers learn?<\/strong><\/p>\n\n\n\n With access to large volumes of data, a Machine Learning algorithm can uncover relationships between variables, classify information, make predictions, and even make decisions. This capability opens the door to countless applications across various fields.<\/p>\n\n\n\n We can say that a Machine Learning project involves three distinct phases:<\/p>\n\n\n\n This article is an introduction to the vast world of Machine Learning, so we will start by discussing the main types of Machine Learning \u2013 supervised learning and unsupervised learning \u2013 explaining what each one means in simple terms.<\/p>\n\n\n\n Supervised Learning (Supervised Learning<\/em>) is a type of Machine Learning where the algorithm is trained with labeled data. In other words, for each set of input variables, there is a known outcome, the target variable. based<\/em>The main objective of these models is to learn the relationship between the input data and the target variable. based<\/em>so as to be able to make accurate predictions.<\/p>\n\n\n\n In general, supervised models fall into two main categories:<\/p>\n\n\n\n A simple way to understand the supervised learning process of these models is to make an analogy with how a child learns the names of fruits. Initially, when seeing a fruit for the first time, the child won\u2019t know how to identify it. However, after being shown different fruits along with their names several times, the child begins to correctly identify each one. With more practice (training), the child becomes able to identify the fruits almost automatically. The same happens with a supervised Machine Learning model.<\/p>\n\n\n\n In practice, it is perfectly possible to build a Machine Learning model to classify images of fruits. This process can be divided into the three phases explained in the introduction:<\/p>\n\n\n\n Phase 1 \u2013 Data collection and preparation<\/strong><\/p>\n\n\n\n Various images of different types of fruits should be collected. To ensure the model learns effectively, it is important that the images are captured under different lighting conditions, from various angles, and with different variations (size, color, background, etc.). Careful preparation ensures the model has representative and diverse data to train on.<\/p>\n\n\n\n Phase 2 \u2013 Model selection and training<\/strong><\/p>\n\n\n\n In this phase, a classification model is chosen since the goal is to identify a category (the fruit\u2019s name) based on the image. The training process involves presenting the images (input variables) to the model along with their correct classification (output variable). At first, the model won\u2019t know how to recognize any fruit, just like the child. But as it gets \u201ctrained\u201d with multiple examples, it begins to understand patterns and associations (for example, if a fruit is yellow, has a certain size, and a slight curve, it is probably a banana).<\/p>\n\n\n\n Phase 3 \u2013 Model evaluation and improvement<\/strong><\/p>\n\n\n\n After training, the model is tested with a new set of images different from those used during training to verify if it can make accurate predictions. If the performance is satisfactory, the model can be considered complete. Otherwise, it may be necessary to optimize it by adjusting model parameters, using more images, or improving the quality and diversity of the images.<\/p>\n\n\n\n Unsupervised Learning (Unsupervised Learning<\/em>) is a type of Machine Learning where the algorithm is trained with unlabeled data. In other words, the input variables have no known associated outcomes. The goal of this type of model is to identify patterns and relationships between variables that are not easily detected by us.<\/p>\n\n\n\n Unlike Supervised models, instead of learning to predict a known value, the model tries to autonomously discover how the data is organized.<\/p>\n\n\n\n In general, unsupervised models fall into two main categories:<\/p>\n\n\n\n <\/p>\n\n\n\n <\/p>\n\n\n\n To make the concept tangible of Unsupervised Learning<\/em>Let\u2019s revisit the analogy of the child and the pieces of fruit to make the concept of Unsupervised Learning tangible. In this case, imagine the child doesn\u2019t know the names of any fruits and is given a bag containing apples, oranges, and bananas along with three empty baskets. The child is then asked to distribute the fruits into the three baskets, grouping those of the same type. It is expected that the child, even without knowing the names of the fruits, will place the apples in one basket, the oranges in another, and the bananas in the last basket, grouping them based on their visual similarities (color, size, shape). A Machine Learning model works similarly: it organizes data based on patterns without needing explicit instructions.<\/p>\n\n\n\n In practice, it is also possible to build a Machine Learning model to cluster images of fruits based on their similarities. This process can be divided into the three phases explained in the introduction:<\/p>\n\n\n\n Phase 1 \u2013 Data collection and preparation<\/strong><\/p>\n\n\n\n Just like in supervised learning, it is necessary to collect a diverse set of fruit images. However, in this case, labeling is not required.<\/strong> the images with the names of the fruits. The focus is on ensuring diversity in the data (variations in color, size, shape, lighting, etc.) so that the model has a solid foundation for identifying patterns.<\/p>\n\n\n\n Phase 2 \u2013 Model selection and training<\/strong><\/p>\n\n\n\n A model is now chosen for clustering<\/em> (clustering), which will analyze the images and group them based on common characteristics. During training, the model receives no indication of how the fruits should be grouped\u2014it is simply allowed to observe the similarities and differences in the data (for example, the model may group all the long, yellow fruits together, which likely corresponds to bananas, even without knowing the name of the fruit).<\/p>\n\n\n\n Phase 3 \u2013 Model evaluation and improvement<\/strong><\/p>\n\n\n\n After the model groups the images, the evaluation can be done by checking whether the formed groups make sense from our point of view (for example, if all the bananas are in the same group). If the clustering is not satisfactory, improvements can be made, such as changing the expected number of groups, adding more images, or using a different algorithm better suited to the nature of the data.<\/p>\n\n\n\n In this article, we explored the fundamentals of Machine Learning \u2013 Supervised Learning<\/strong> and Unsupervised Learning<\/strong>While Supervised Learning is based on labeled data and well-defined objectives (such as classification or value prediction), Unsupervised Learning seeks \u201chidden\u201d patterns in the data, revealing relationships that are not always obvious.<\/p>\n\n\n\n In both cases, the goal is to transform data into useful information\u2014a challenge increasingly present in modern organizations. However, the success of any project in this area does not depend solely on the algorithm used, but above all, on the quality of the data.<\/p>","protected":false},"excerpt":{"rendered":" Transformar dados em intelig\u00eancia Vivemos numa era em que os dados s\u00e3o gerados a uma velocidade nunca vista. Transformar uma enorme quantidade de dados em bruto em informa\u00e7\u00e3o \u00fatil para o apoio \u00e0 tomada de decis\u00e3o \u00e9 um dos maiores desafios das organiza\u00e7\u00f5es atuais. Mas como \u00e9 que os dados nos podem ajudar a tomar […]<\/p>","protected":false},"author":1,"featured_media":5104,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1],"tags":[72,73,74],"class_list":["post-5102","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-business-intelligence","tag-machine-learning","tag-supervised-learning","tag-unsupervised-learning"],"acf":[],"yoast_head":"\r\n\n
Supervised Learning<\/h1>\n\n\n\n
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<\/figure><\/div>\n\n\nUnsupervised Learning<\/strong><\/h1>\n\n\n\n
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<\/figure><\/div>\n\n\nThe most important thing\u2026 is not the algorithm<\/h1>\n\n\n\n