Update pages/Machine Learning vs Deep Learning.py

pages/Machine Learning vs Deep Learning.py

@@ -1,8 +1,14 @@
 import streamlit as st
+
+# Adding the introduction part
 st.markdown("""
-    <p style='font-size: 24px;'>Earlier, we discussed how <b>Machine Learning (ML)</b> and <b>Deep Learning (DL)</b> are two powerful tools in the field of <b>Artificial Intelligence (AI)</b>. But how do we better understand their differences? Let’s use an analogy:</p>""")
+    <p style='font-size: 24px;'>Earlier, we discussed how <b>Machine Learning (ML)</b> and <b>Deep Learning (DL)</b> are two powerful tools in the field of <b>Artificial Intelligence (AI)</b>. But how do we better understand their differences? Let’s use an analogy:</p>
+""", unsafe_allow_html=True)
+
+# Adding the analogy heading
 st.markdown("<h2 style='color: #3498DB; font-size: 30px; font-weight: bold;'>✨ Imagine This: Chefs in the Kitchen </h2>", unsafe_allow_html=True)
 
+# Adding the analogy content
 st.markdown("""
     <p style='font-size: 24px;'>Two chefs are in the same kitchen. One chef is highly skilled and prepares a dish every day, experimenting with different flavors and ingredients. The second chef, who is not very experienced, wants to recreate the dish but hasn’t been as consistent with their practice. To do this, they borrow the first chef's recipe to copy it. To help them recreate the dish, they use two tools: a <b>recipe book</b> and a <b>set of cooking tools</b>. While both tools serve the same purpose of helping them cook, their functionality differs:</p>
     
@@ -18,10 +24,9 @@ st.markdown("""
     </ul>
 """, unsafe_allow_html=True)
 
-
+# Adding the table comparison content
 markdown_content = """
 # Machine Learning vs Deep Learning
-
 | Features                | Machine Learning (ML)                                      | Deep Learning (DL)                                      |
 |----------------------|------------------------------------------------------------|---------------------------------------------------------|
 | **Learning Approach** | Uses a statistical approach to analyze data and make predictions. | Uses neural networks to automatically learn patterns. |