Update pages/DIFFERENCES_BETWEEN_ML&DL.py

pages/DIFFERENCES_BETWEEN_ML&DL.py

@@ -1,8 +1,5 @@
 import streamlit as st
 
-# Static background image URL
-
-
 # Dynamic CSS
 custom_css = """
 <style>
@@ -36,37 +33,34 @@ custom_css = """
         font-size: 2rem; 
         text-shadow: 1px 1px 3px rgba(0, 0, 0, 0.7);
     }
-    .division p, .division ul li {
+    .division ul li {
         font-size: 1.2rem; 
         line-height: 1.7; 
+        margin-bottom: 8px;
     }
 </style>
 """
+
 # Inject CSS into Streamlit app
 st.markdown(custom_css, unsafe_allow_html=True)
 
 # Header Section
 st.markdown("<h1>DIFFERENCES BETWEEN ML AND DL</h1>", unsafe_allow_html=True)
 
+# Content for Machine Learning
 st.markdown(
     """
     <div class="division">
-        <h2>The points in machine learning?</h2>
-        <p>
-
-        
-        </p>
+        <h2>Key Points in Machine Learning</h2>
         <ul>
-             <li><strong>Data Points:</strong>Can train only on small data points .</li>
-             <li><strong>Hardware for Training:</strong>Can train on CPU.</li>
-             <li><strong>Training Time:</strong>Requires less time due to smaller sizes.</li>
-             <li><strong>Algorithm Complexity:</strong>Machine learning algorithms can range from simple linear models to more complex models such as decision trees and random forests..</li>
-             <li><strong>Analysis Complexity:</strong>Involves training algorithm to identify patterns and relationship in data..</li>
-             <li><strong>Application Areas:</strong>It is used for a wide range of applications, such as regression, classification, and clustering.</li>
-                            </ul>
+            <li><strong>Data Points:</strong> Machine Learning models can be trained on smaller datasets.</li>
+            <li><strong>Hardware for Training:</strong> Training can be done on standard CPUs.</li>
+            <li><strong>Training Time:</strong> Requires less time due to smaller dataset sizes and simpler algorithms.</li>
+            <li><strong>Algorithm Complexity:</strong> Includes simpler models like linear regression and more complex ones like decision trees or random forests.</li>
+            <li><strong>Analysis Complexity:</strong> Involves identifying patterns and relationships in data.</li>
+            <li><strong>Application Areas:</strong> Widely used for tasks such as regression, classification, and clustering.</li>
+        </ul>
     </div>
     """,
     unsafe_allow_html=True
 )
-
-