Update pages/Machine Learning vs Deep Learning.py

pages/Machine Learning vs Deep Learning.py

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+import streamlit as st
+
+markdown_content = """
+# Machine Learning vs Deep Learning
+
+## Comparison Table
+
+| Aspect                | 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. |
+| **Data Requirement** | Works well with smaller datasets.                          | Requires large amounts of data to perform well.         |
+| **Feature Engineering** | Requires manual feature selection and extraction.       | Automatically learns features from raw data.            |
+| **Interpretability** | Easier to interpret and explain model decisions.          | Harder to interpret due to complex layers in the network. |
+| **Computation Power** | Can run on CPUs (low computational power).               | Requires GPUs/TPUs (high computational power).         |
+| **Algorithms Used** | Uses models like KNN, Decision Trees, Linear Regression.  | Uses ANN, CNN, RNN for feature extraction and learning. |
+| **Training Time** | Faster training due to simpler computations.                | Longer training time due to deep layers and complex processing. |
+| **Data Types Processed** | Works with structured/tabular data.                  | Works with images, videos, text, and audio.            |
+"""
+
+st.markdown(markdown_content)