Delete Introduction to ML.md

Introduction to ML.md

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-import streamlit as st  
-
-st.markdown("""
-    <h1>Introduction to Machine Learning 🚀</h1>
-    <p>Machine Learning (ML) is a branch of artificial intelligence that allows computers to learn from data and make predictions. It is widely used in applications like recommendation systems, speech recognition, and self-driving cars.</p>
-
-    <h2>📌 Why Machine Learning?</h2>
-    <ul>
-        <li>Automates decision-making</li>
-        <li>Helps in data-driven predictions</li>
-        <li>Powers AI applications like chatbots and virtual assistants</li>
-    </ul>
-
-    <h2>🔹 Types of Machine Learning</h2>
-
-    <h3>1️⃣ Supervised Learning</h3>
-    <p>In supervised learning, the model is trained on <b>labeled data</b>. Example algorithms include:</p>
-    <ul>
-        <li><b>Linear Regression</b> (for predicting continuous values)</li>
-        <li><b>Decision Trees</b> (for classification problems)</li>
-    </ul>
-
-    <h3>2️⃣ Unsupervised Learning</h3>
-    <p>Here, the model finds patterns in <b>unlabeled data</b>. Example algorithms:</p>
-    <ul>
-        <li><b>Clustering (K-Means, DBSCAN)</b></li>
-        <li><b>Principal Component Analysis (PCA) for dimensionality reduction</b></li>
-    </ul>
-
-    <h3>3️⃣ Reinforcement Learning</h3>
-    <p>The model learns by interacting with an environment and <b>receiving rewards</b>. Used in:</p>
-    <ul>
-        <li><b>Game playing (AlphaGo, Chess AI)</b></li>
-        <li><b>Robotics and automation</b></li>
-    </ul>
-
-    <h2>🚀 How to Get Started with ML?</h2>
-    <ol>
-        <li>Learn Python and essential libraries: <code>numpy</code>, <code>pandas</code>, <code>scikit-learn</code></li>
-        <li>Explore datasets from <b>Kaggle</b> and <b>UCI Machine Learning Repository</b></li>
-        <li>Try building small projects like <b>house price prediction</b> or <b>spam detection</b></li>
-    </ol>
-
-    <h2>🎯 Conclusion</h2>
-    <p>Machine learning is a powerful tool that is transforming industries. Learning ML opens opportunities in AI development, data science, and automation.</p>
-
-    <p><b>Stay tuned for more insights! 😊</b></p>
-""", unsafe_allow_html=True)
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