<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Unsupervised Learning on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/unsupervised-learning/</link><description>Recent content in Unsupervised Learning on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Sun, 18 Jan 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/unsupervised-learning/index.xml" rel="self" type="application/rss+xml"/><item><title>Chapter 8: Unsupervised Learning: Finding Hidden Patterns</title><link>https://ai-blog.noorshomelab.dev/ai-ml-journey-2026/unsupervised-learning-intro/</link><pubDate>Sun, 18 Jan 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/ai-ml-journey-2026/unsupervised-learning-intro/</guid><description>&lt;h2 id="introduction-the-detective-of-data"&gt;Introduction: The Detective of Data&lt;/h2&gt;
&lt;p&gt;Welcome back, future AI wizard! So far in our journey, we&amp;rsquo;ve explored the exciting world of Supervised Learning. Remember how we trained models with labeled data, like teaching a child to identify cats by showing them pictures &lt;em&gt;labeled&lt;/em&gt; &amp;ldquo;cat&amp;rdquo;? We had a &amp;ldquo;teacher&amp;rdquo; telling the model what the correct answer was.&lt;/p&gt;
&lt;p&gt;But what if there&amp;rsquo;s no teacher? What if you have a huge pile of information and no one tells you what&amp;rsquo;s what? This is where a truly fascinating side of Machine Learning comes in: &lt;strong&gt;Unsupervised Learning&lt;/strong&gt;.&lt;/p&gt;</description></item><item><title>Supervised vs. Unsupervised Learning: Two Ways AI Learns</title><link>https://ai-blog.noorshomelab.dev/ai-ml-journey-2026/supervised-unsupervised-learning/</link><pubDate>Sun, 18 Jan 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/ai-ml-journey-2026/supervised-unsupervised-learning/</guid><description>&lt;p&gt;Welcome back, future AI wizard! You&amp;rsquo;re doing an absolutely fantastic job navigating the exciting world of Artificial Intelligence. In our last chapters, we learned about what AI and Machine Learning are, how they learn from data, and what makes a &amp;ldquo;model&amp;rdquo; tick. You&amp;rsquo;ve already grasped some really big ideas, and that&amp;rsquo;s something to be proud of!&lt;/p&gt;
&lt;p&gt;Today, we&amp;rsquo;re going to dive into two main &amp;ldquo;styles&amp;rdquo; or &amp;ldquo;approaches&amp;rdquo; that AI uses to learn: &lt;strong&gt;Supervised Learning&lt;/strong&gt; and &lt;strong&gt;Unsupervised Learning&lt;/strong&gt;. Think of them as two different ways a student might learn a new subject. Sometimes you learn with a teacher guiding you every step of the way, and sometimes you just explore and figure things out on your own. These two styles are fundamental to almost all AI systems you encounter!&lt;/p&gt;</description></item></channel></rss>