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<!DOCTYPE html>
<html>
<style>
body{
background-image: url(images/background.jpeg);
}
p{
font-size:30px;
}
h1,h2,h3{
font-size:45px;
}
ul{
font-size:30px;
}
</style>
<head>
<title>The ML Guide</title>
</head>
<body style="height:100%;" border="1">
<table width="90%" align="center" border="0" margin="50" height="100%">
<tr>
<td>
<h1 align="center"><u>ML Verse</u></h1>
<div align="center">
<img id="computer" src="images/ml.jpg" alt="A Computer" width="300px" border="0" style="border-radius:50%;">
</div>
<h2>What is Machine Learning?</h2>
<p>It is a branch of Artificial Intelligence (AI) that is used to build systems that rely on data provided to them.
Instead of a specific rules to be written for a computer to do its job, the computer is given a large amount of data and it uses
algorithms to discover the rules and patterns itself.
</p>
<hr>
<h2>Traditional Programming vs. Machine Learning</h2>
<p>Machine Learning is such a paradigm shift in the world of technology.
Following provides a comprehensive comparison of traditional programming and machine learning: </p>
<div align="center"><h3 style="font-size:40px;">❀ Traditional Programming: Logic first</h3></div>
<blockquote>
<p>In this approach, a programmer writes a manual script (rules) to process data.</p>
<ul>
<li><strong>Example: </strong> A banking app. <br>
The programmer writes a rule: "If account balance < $0, then send a 'Low Balance' alert."
The computer simply follows that instruction every single time.
</li>
</ul>
</blockquote>
<div align="center"><h3 style="font-size:40px;">❀ Machine Learning: Data first</h3></div>
<blockquote>
<p>We give the computer the data and the desired answers, and it figures out the patterns itself.</p>
<ul>
<li><strong>Example: </strong> A spam filter. <br>
Instead of writing a rule for every possible spam word, you show the computer 10, 000 "Spam" emails and 10,000 "Not Spam" emails.
The computer identifies the subtle patterns that make an email spam
</li>
</ul>
</blockquote>
<hr>
<h2>Basic Terminology</h2>
<h3 style="font-size:40px;">1. The Data Building Blocks</h3>
<ul>
<li><strong>Feature: </strong> An individual measurable property or "variable" of the data you are analyzing.</li>
<li><strong>Label: </strong> The "answer" or the target you are trying to predict (e.g., the actual "price" of the house).</li>
<li><strong>Dataset: </strong> The collection of examples used for learning. It is usually split into:
<ul>
<li>Training Set: Data used to teach the model.</li>
<li>Test Set: Data used to see how well the model performs.</li>
</ul>
</li>
</ul>
<h3 style="font-size:40px;">2. Training Terms</h3>
<ul>
<li><strong>Algorithm: </strong>The specific mathematical procedure used to find patterns in data (e.g., Linear Regression, Decision Trees).</li>
<li><strong>Model: </strong> The "finished product." It is what you get after you run an algorithm on your training data.</li>
<li><strong>Epoch: </strong>One full pass of the entire training dataset through the model. If you train for 10 epochs, the model has "seen" the data 10 times.</li>
<li><strong>Loss Function: </strong> A mathematical formula that calculates how "wrong" the model's prediction was. The goal of training is to make this number as small as possible.</li>
</ul>
<hr>
<h2>The ML Workflow</h2>
<div align="center"><img src="images/flowchart.png" alt="ML Workflow" border="1"></div>
<hr>
<h2>Types of Machine Learning Systems</h2>
<ul>
<li><a href="supervised.html" target="_blank">Supervised Learning</a></li>
<li><a href="unsupervised.html" target="_blank">Unsupervised Learning</a></li>
<li><a href="reinforcement.html" target="_blank">Reinforcement Learning</a></li>
<li><a href="semi-supervised.html" target="_blank">Semi-Supervised Learning</a></li>
</ul>
<hr>
<h2>Watch this YT Video!</h2>
<section id="youtube-video">
<div align="center"><iframe width="560" height="315"
src="https://youtube.com/embed/ukzFI9rgwfU"
title="youtube video player"
frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
allowfullscreen>
</iframe></div>
</section>
<hr>
<section>
<h3>Reach Out</h3>
<div align="center">
<form style="max-width: 500px;">
<label for="fname">First Name:</label><br>
<input type="text" id="fname" name="firstname" placeholder="Your name"
style="width: 100%; padding: 12px; margin: 8px 0; font-size: 16px; border: 1px solid #ccc; border-radius: 4px;"><br>
<label for="email">Your email address:</label><br>
<input type="email" id="email" name="email" placeholder="example@gmail.com" required
style="width: 100%; padding: 12px; margin: 8px 0; font-size: 16px; border: 1px solid #ccc; border-radius: 4px;"><br>
<label for="subject">Reason for contact:</label><br>
<textarea id="message" name="subject" rows="8" placeholder="Your reason" required
style="width: 100%; padding: 12px; margin: 8px 0; font-size: 16px; border: 1px solid #ccc; border-radius: 4px;"></textarea><br>
<input type="submit" value="Submit Message"
style="width: 100%; background-color: #bbb; color: white; padding: 14px; border: none; border-radius: 4px; cursor: pointer; font-size: 18px; font-weight: bold;">
</form>
</div>
</section>
</td>
</tr>
</table>
</body>
</html>