Update Introduction to ML.md

Introduction to ML.md

@@ -1,36 +1,49 @@
-# Introduction to Machine Learning 🚀  
-
-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.  
-
-## 📌 Why Machine Learning?  
-Machine learning is important because it:  
-- Automates decision-making  
-- Helps in data-driven predictions  
-- Powers AI applications like chatbots and virtual assistants  
-
-## 🔹 Types of Machine Learning  
-### 1️⃣ Supervised Learning  
-In supervised learning, the model is trained on **labeled data**. Example algorithms include:  
-- **Linear Regression** (for predicting continuous values)  
-- **Decision Trees** (for classification problems)  
-
-### 2️⃣ Unsupervised Learning  
-Here, the model finds patterns in **unlabeled data**. Example algorithms:  
-- **Clustering (K-Means, DBSCAN)**  
-- **Principal Component Analysis (PCA) for dimensionality reduction**  
-
-### 3️⃣ Reinforcement Learning  
-The model learns by interacting with an environment and **receiving rewards**. Used in:  
-- **Game playing (AlphaGo, Chess AI)**  
-- **Robotics and automation**  
-
-## 🚀 How to Get Started with ML?  
-1. Learn Python and essential libraries:  
-   - `numpy`, `pandas`, `scikit-learn`  
-2. Explore datasets from **Kaggle** and **UCI Machine Learning Repository**  
-3. Try building small projects like **house price prediction** or **spam detection**  
-
-## 🎯 Conclusion  
-Machine learning is a powerful tool that is transforming industries. Learning ML opens opportunities in AI development, data science, and automation.  
-
-**Stay tuned for more insights! 😊**  
+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)
+