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    <title>DEV Community: Shubhanshu Trivedi</title>
    <description>The latest articles on DEV Community by Shubhanshu Trivedi (@shub_03).</description>
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      <title>DEV Community: Shubhanshu Trivedi</title>
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      <title>What is Machine Learning really about ?</title>
      <dc:creator>Shubhanshu Trivedi</dc:creator>
      <pubDate>Sat, 03 May 2025 17:03:45 +0000</pubDate>
      <link>https://dev.to/shub_03/what-is-machine-learning-really-about--349j</link>
      <guid>https://dev.to/shub_03/what-is-machine-learning-really-about--349j</guid>
      <description>&lt;p&gt;📊 &lt;strong&gt;Machine learning&lt;/strong&gt; is about &lt;strong&gt;using data to make smart predictions or decisions&lt;/strong&gt;. &lt;/p&gt;

&lt;p&gt;But other fields (like &lt;strong&gt;statistics&lt;/strong&gt; or &lt;strong&gt;psychology&lt;/strong&gt;) also work with data, so how they are different than Machine Learning ❓&lt;/p&gt;

&lt;p&gt;And the correct answer is - ✅ &lt;strong&gt;&lt;em&gt;their goals are different&lt;/em&gt;.&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;To understand it much more clearly let me give an example:&lt;/p&gt;

&lt;p&gt;🌻 &lt;strong&gt;Imagine you have a garden.&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;You notice some flowers are growing really big, and some are small. Now you're curious - &lt;strong&gt;why are some flowers bigger than others?&lt;/strong&gt;&lt;/p&gt;




&lt;p&gt;📈 &lt;strong&gt;In Statistics:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;You count how much 🌊 water, ☀️ sunlight, and 🌱 fertilizer each flower got.&lt;br&gt;
Then you say:&lt;/p&gt;

&lt;p&gt;“Ah! When I give more water, flowers grow bigger!”&lt;/p&gt;

&lt;p&gt;You make a rule (a model) to explain what makes the flowers grow.&lt;br&gt;
So the &lt;strong&gt;"why"&lt;/strong&gt; here is:&lt;/p&gt;

&lt;p&gt;“Because they got more water and sunlight.”&lt;/p&gt;

&lt;p&gt;So you create a model to explain the relationship — you’re interested in the &lt;strong&gt;why&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;✅ &lt;strong&gt;Goal:&lt;/strong&gt; Understand the cause behind the outcome.&lt;/p&gt;




&lt;p&gt;🧠 &lt;strong&gt;In Psychology:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Let’s say you're studying how your friend feels.&lt;br&gt;
You notice:&lt;/p&gt;

&lt;p&gt;When your friend doesn't sleep, they get cranky 😠.&lt;/p&gt;

&lt;p&gt;You ask:&lt;/p&gt;

&lt;p&gt;“Why is my friend cranky today?”&lt;/p&gt;

&lt;p&gt;And you think:&lt;/p&gt;

&lt;p&gt;“Maybe it’s because they didn’t sleep well 😴.”&lt;/p&gt;

&lt;p&gt;So you're trying to find the real reason behind feelings or behavior.&lt;br&gt;
The "why" is:&lt;/p&gt;

&lt;p&gt;“Because they didn’t sleep.”&lt;/p&gt;

&lt;p&gt;You try to find the real reason behind their feelings or behavior.&lt;/p&gt;

&lt;p&gt;✅ &lt;strong&gt;Goal:&lt;/strong&gt; Understand human emotions and behavior — again, the &lt;strong&gt;why&lt;/strong&gt;.&lt;/p&gt;




&lt;p&gt;🤖 &lt;strong&gt;In Machine Learning:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;You give the computer &lt;strong&gt;LOTS of examples:&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;How much water each flower got 💧.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;How big it grew 🌼&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;The computer learns to guess flower size just by looking at water and sunlight —  &lt;strong&gt;but it doesn’t really know why.&lt;/strong&gt; &lt;/p&gt;

&lt;p&gt;It just learns to make good guesses.&lt;br&gt;
So if you ask it:&lt;/p&gt;

&lt;p&gt;“Why is this flower big?”&lt;/p&gt;

&lt;p&gt;It might say:&lt;/p&gt;

&lt;p&gt;“I don’t know, but I saw something like this before, and it turned out big.”&lt;/p&gt;

&lt;p&gt;✅ &lt;strong&gt;Goal:&lt;/strong&gt; Make accurate predictions, even if it doesn’t understand why.&lt;/p&gt;

&lt;p&gt;So, the &lt;strong&gt;goal&lt;/strong&gt; is the key difference in Machine learning vs Others (like statistics or psychology).&lt;/p&gt;

&lt;p&gt;✨ &lt;strong&gt;Machine Learning is less about &lt;em&gt;explaining why&lt;/em&gt; something happens and more about &lt;em&gt;guessing&lt;/em&gt; what will happen next, based on patterns in data.&lt;/strong&gt;&lt;/p&gt;

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      <category>tutorial</category>
      <category>machinelearning</category>
      <category>chatgpt</category>
      <category>beginners</category>
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