<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Model Development on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/model-development/</link><description>Recent content in Model Development 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/model-development/index.xml" rel="self" type="application/rss+xml"/><item><title>Chapter 4: How Machines Learn: Training and Prediction Explained</title><link>https://ai-blog.noorshomelab.dev/ai-ml-journey-2026/training-prediction-explained/</link><pubDate>Sun, 18 Jan 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/ai-ml-journey-2026/training-prediction-explained/</guid><description>&lt;h2 id="chapter-4-how-machines-learn-training-and-prediction-explained"&gt;Chapter 4: How Machines Learn: Training and Prediction Explained&lt;/h2&gt;
&lt;p&gt;Welcome back, future AI explorer! In our last chapter, we started to understand what an AI &amp;ldquo;model&amp;rdquo; is – essentially, a smart recipe or a set of rules that can make decisions or predictions. But how does this &amp;ldquo;recipe&amp;rdquo; get written? How does a model become smart? That&amp;rsquo;s exactly what we&amp;rsquo;ll uncover in this chapter: the fascinating processes of &lt;strong&gt;training&lt;/strong&gt; and &lt;strong&gt;prediction&lt;/strong&gt;.&lt;/p&gt;</description></item></channel></rss>