{"id":1106,"date":"2016-09-29T18:28:11","date_gmt":"2016-09-29T18:28:11","guid":{"rendered":"http:\/\/www.csestack.org\/?p=1106"},"modified":"2021-03-04T09:16:19","modified_gmt":"2021-03-04T03:46:19","slug":"types-machine-learning-algorithms-examples","status":"publish","type":"post","link":"https:\/\/www.csestack.org\/types-machine-learning-algorithms-examples\/","title":{"rendered":"4 Different Types of Machine Learning Algorithms with Examples"},"content":{"rendered":"<p><img fetchpriority=\"high\" decoding=\"async\" class=\"aligncenter wp-image-5358 size-full\" src=\"https:\/\/www.csestack.org\/wp-content\/uploads\/2019\/06\/Machine-Learning.png\" alt=\"machine learning\" width=\"740\" height=\"480\" srcset=\"https:\/\/www.csestack.org\/wp-content\/uploads\/2019\/06\/Machine-Learning.png 740w, https:\/\/www.csestack.org\/wp-content\/uploads\/2019\/06\/Machine-Learning-300x195.png 300w, https:\/\/www.csestack.org\/wp-content\/uploads\/2019\/06\/Machine-Learning-260x170.png 260w\" sizes=\"(max-width: 740px) 100vw, 740px\" \/><\/p>\n<p>Do you want to learn more about Machine Learning (ML)? What is Machine Learning? What are the different types of Machine Learning algorithms?<\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_2 counter-hierarchy ez-toc-counter ez-toc-custom ez-toc-container-direction\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<label for=\"ez-toc-cssicon-toggle-item-69fd5f2d9a827\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #000000;color:#000000\" 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: #000000;color:#000000\" 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><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-69fd5f2d9a827\"  aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.csestack.org\/types-machine-learning-algorithms-examples\/#Introduction_to_ML\" >Introduction to ML<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.csestack.org\/types-machine-learning-algorithms-examples\/#Types_of_Machine_Learning_Algorithms\" >Types of Machine Learning Algorithms<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.csestack.org\/types-machine-learning-algorithms-examples\/#Supervised_Machine_Learning\" >Supervised Machine Learning<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.csestack.org\/types-machine-learning-algorithms-examples\/#Unsupervised_Machine_Learning\" >Unsupervised Machine Learning<\/a><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n<h3 style=\"text-align: center;\"><span class=\"ez-toc-section\" id=\"Introduction_to_ML\"><\/span>Introduction to ML<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Machine Learning has been a new hype in the field of computer science for a year along with other trending topics such as the Internet of Things, Artificial Intelligence, <a href=\"http:\/\/www.csestack.org\/category\/big-data\/\">Big Data<\/a>&#8230;<\/p>\n<p>But what is Machine Learning for beginners?<\/p>\n<blockquote><p>Machine Learning is developing a computer program that improves automatically with experience.<\/p><\/blockquote>\n<p>This is a general definition according to me so that it is easily understood by freshers.<\/p>\n<p>By the field of usage and kind of data we are using as input, we can modify this definition accordingly.<\/p>\n<p>For example, Genetic programming is the field of Machine Learning where you essentially evolve a program to complete a task while <a href=\"https:\/\/www.csestack.org\/neural-networks-python\/\">Neural networks<\/a> modify their parameters automatically in response to prepared stimuli and expected a response.<\/p>\n<p>Machine Learning can be divided into two following categories based on the type of data we are using as input:<\/p>\n<h3 style=\"text-align: center;\"><span class=\"ez-toc-section\" id=\"Types_of_Machine_Learning_Algorithms\"><\/span>Types of Machine Learning Algorithms<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>There are two main types of machine learning algorithms.<\/p>\n<ul>\n<li>Supervised learning \u2013 It is a task of inferring a function from labeled training data.<\/li>\n<li>Unsupervised learning \u2013 It is the task of inferring from a data set having input data without labeled response.<\/li>\n<\/ul>\n<p><strong>What is a Supervised Machine Learning Algorithm?<\/strong><\/p>\n<p>It is a task of inferring a function from labeled training data.<\/p>\n<p><strong>What is an Unsupervised Machine Learning Algorithm?<\/strong><\/p>\n<p>It is the task of inferring from a data set having input data without labeled response.<\/p>\n<p>Both algorithms are further classified into subtypes.<\/p>\n<h4 class=\"note\"><span class=\"ez-toc-section\" id=\"Supervised_Machine_Learning\"><\/span>Supervised Machine Learning<span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p>Supervised learning is where you use an algorithm to learn a mapping function from input to output.<\/p>\n<pre>Y = f(x) where,\nx \u2013 input variable\nY \u2013 output variable\n<\/pre>\n<p>The goal is to approximate the mapping function so well that when you have new input data that you can predict the output variable for that data.<\/p>\n<p>Supervised learning problems can be further divided into regression and classification problems.<\/p>\n<p><strong>1. Classification:<\/strong><\/p>\n<p>Classification is when we have discrete-valued output.<\/p>\n<p>For example,<\/p>\n<ul>\n<li>When you receive an email, it falls under the &#8220;spam&#8221; or &#8220;inbox&#8221; folder. This means email is classified into two types- &#8220;spam&#8221; or &#8220;inbox&#8221;.<\/li>\n<li>Based on the color of the ball, it can be classified into &#8220;red&#8221;, &#8220;blue&#8221;, &#8220;green&#8221; and &#8220;all other colors&#8221;.<\/li>\n<\/ul>\n<p><strong>2. Regression:<\/strong><\/p>\n<p>A regression is when we have continuous-valued output.<\/p>\n<p>For example,<\/p>\n<ul>\n<li>The price of the house each year.<\/li>\n<li>Increment in the height of a person over the age.<\/li>\n<\/ul>\n<p><strong>Algorithm Examples:<\/strong><\/p>\n<p>Some example of supervised learning algorithms are:<\/p>\n<ul>\n<li><a href=\"http:\/\/www.csestack.org\/linear-regression-example\/\">Linear regression example<\/a> for regression problems.<\/li>\n<li>Logistic regression for classification problems.<\/li>\n<li>Random forest for classification and regression problems.<\/li>\n<\/ul>\n<h4 class=\"note\"><span class=\"ez-toc-section\" id=\"Unsupervised_Machine_Learning\"><\/span>Unsupervised Machine Learning<span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p>Unsupervised learning is where you only have input data and there is no corresponding output.<\/p>\n<p>The goal of unsupervised learning is to recognize structure in the data in order to learn more about data.<\/p>\n<p>Unsupervised learning problems can be further divided into clustering and association problems.<\/p>\n<p><strong>3. Clustering:<\/strong><\/p>\n<p>A clustering problem is where you want to recognize the inherent grouping in the data.<\/p>\n<p>For example,<\/p>\n<ul>\n<li>grouping customers by who tends to eat veg food items in a restaurant<\/li>\n<\/ul>\n<p><strong>4. Association:<\/strong><\/p>\n<p>An association rule learning problem is where you want to discover rules that describe your data.<\/p>\n<p>For example,<\/p>\n<ul>\n<li>people that buy laptop also more likely tend to buy antivirus software<\/li>\n<\/ul>\n<p><strong>Algorithm Examples:<\/strong><\/p>\n<p>Some example of unsupervised learning algorithms are:<\/p>\n<ul>\n<li>K-means for clustering problems<\/li>\n<li>Hierarchical clustering<\/li>\n<li>Density-Based Spatial Clustering of Application with Noise (DBSCAN)<\/li>\n<li>Anomaly detection<\/li>\n<\/ul>\n<p>These Machine Learning algorithms are very useful whether it&#8217;s in <a href=\"https:\/\/www.csestack.org\/meera-machine-learning-assistant-bot-nlp\/\">Natual Language processing<\/a> or <a href=\"https:\/\/www.csestack.org\/machine-learning-genomics-genetics\/\">the study of Genetics<\/a>.<\/p>\n<p>This is all about different types of machine learning algorithms. We will cover more insight into each topic and algorithm in upcoming articles.<\/p>\n<p>If you have any questions, write in the comment.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>What are the different types of machine learning algorithms? Explain with examples. What are supervised and unsupervised learning algorithms?<\/p>\n","protected":false},"author":9,"featured_media":5358,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[129],"tags":[130],"class_list":["post-1106","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-machine-learning","tag-machine-learning"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>4 Different Types of Machine Learning Algorithms with Examples<\/title>\n<meta name=\"description\" content=\"What are the different types of machine learning algorithms? Explain with examples. 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Explain with examples. 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