1.0KaLabshttps://karthicklakshmanan.comkarthickhttps://karthicklakshmanan.com/index.php/author/karthick/rich600338<blockquote class="wp-embedded-content" data-secret="ZsF1sJ8Ogh"><a href="https://karthicklakshmanan.com/index.php/knowledge-base/common-machine-learning-models/">Common Machine learning models</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://karthicklakshmanan.com/index.php/knowledge-base/common-machine-learning-models/embed/#?secret=ZsF1sJ8Ogh" width="600" height="338" title="“Common Machine learning models” — KaLabs" data-secret="ZsF1sJ8Ogh" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe><script> /*! This file is auto-generated */ !function(d,l){"use strict";l.querySelector&&d.addEventListener&&"undefined"!=typeof URL&&(d.wp=d.wp||{},d.wp.receiveEmbedMessage||(d.wp.receiveEmbedMessage=function(e){var t=e.data;if((t||t.secret||t.message||t.value)&&!/[^a-zA-Z0-9]/.test(t.secret)){for(var s,r,n,a=l.querySelectorAll('iframe[data-secret="'+t.secret+'"]'),o=l.querySelectorAll('blockquote[data-secret="'+t.secret+'"]'),c=new RegExp("^https?:$","i"),i=0;i<o.length;i++)o[i].style.display="none";for(i=0;i<a.length;i++)s=a[i],e.source===s.contentWindow&&(s.removeAttribute("style"),"height"===t.message?(1e3<(r=parseInt(t.value,10))?r=1e3:~~r<200&&(r=200),s.height=r):"link"===t.message&&(r=new URL(s.getAttribute("src")),n=new URL(t.value),c.test(n.protocol))&&n.host===r.host&&l.activeElement===s&&(d.top.location.href=t.value))}},d.addEventListener("message",d.wp.receiveEmbedMessage,!1),l.addEventListener("DOMContentLoaded",function(){for(var e,t,s=l.querySelectorAll("iframe.wp-embedded-content"),r=0;r<s.length;r++)(t=(e=s[r]).getAttribute("data-secret"))||(t=Math.random().toString(36).substring(2,12),e.src+="#?secret="+t,e.setAttribute("data-secret",t)),e.contentWindow.postMessage({message:"ready",secret:t},"*")},!1)))}(window,document); </script> Machine learning models can be grouped based on their characteristics, underlying algorithms, and the types of tasks they are designed to solve. Here are some common groupings of machine learning models: 1. Supervised Learning Models: 2. Unsupervised Learning Models: 3. Ensemble Models: 4. Regression Models: 5. Classification Models: 6. Clustering Models: 7. Dimensionality Reduction Models: 8. Time Series Models: 9. Natural Language Processing (NLP) Models: 10. Recommender Systems: Description:Models designed to recommend items or content to users based on their preferences or behavior. Examples: Collaborative Filtering – Content-Based Filtering – Hybrid Recommender Systems These groupings provide a high-level categorization of machine learning models. Within each group, there can be variations and combinations of algorithms tailored to specific tasks and challenges. The choice of model depends on the characteristics of the data and the objectives of the machine learning project.