pages/1_Introduction.py

import streamlit as st
from PIL import Image
import io


# Title and introduction
st.title('Understanding Machine Learning: Teaching Machines Like Children')

st.image("https://huggingface.co/spaces/Phani1008/Machine_Learning/resolve/main/images/DALL%C2%B7E%202024-12-27%2018.19.15%20-%20A%20warm%20and%20cozy%20scene%20of%20a%20father%20sitting%20with%20his%203-year-old%20child%20in%20a%20living%20room%2C%20showing%20pictures%20of%20dogs%20and%20cats.%20The%20father%20is%20pointing%20at%20a%20p.jpg",width = 600)


st.write("""
Machine Learning (ML) is like teaching a child to recognize things by showing examples. Imagine a father showing pictures of dogs and cats to his child, saying 'This is a dog' or 'This is a cat'. The child learns by associating the image with the label. Similarly, machines learn from data through two main types of learning: **Supervised** and **Unsupervised Learning**.

In this app, we'll use the analogy of a father teaching his child to explain these key ML concepts.
""")

# Display the image
# Supervised Learning Explanation
st.header('Supervised Learning')
st.write("""
In supervised learning, the machine learns from labeled data. This process is similar to how the child learns when the father points to a picture and says 'This is a dog'.

**Analogy:**
- X_i: A picture of a dog shown to the child (input or feature)
- Y_i: The father saying 'This is a dog' (output or label)

Over time, the child (or machine) becomes better at recognizing dogs, even if shown new pictures. The machine adjusts its internal model to predict the correct label for unseen data.
""")

# Unsupervised Learning Explanation
st.header('Unsupervised Learning')
st.write("""
In unsupervised learning, the machine is not given explicit labels. It tries to identify patterns and groupings on its own. This is like the child noticing differences between animals without being told their names.

**Analogy:**
- X_i: A set of pictures of animals (without labels)
- Y_i: The child starts grouping pictures into categories like 'dogs', 'cats', etc., even if they don't know the exact names.

Unsupervised learning helps the machine find structure in data without needing labeled examples.
""")

# Conclusion
st.write("""
Machine Learning mirrors how humans, especially children, learn by observing and being guided. This analogy helps simplify the complex concepts behind teaching machines to recognize patterns and make decisions. Just like the child learns over time, machines improve as they are exposed to more data.
""")