Update app.py

app.py

@@ -1,6 +1,60 @@
 import streamlit as st
-import math
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', math.sqrt(x))
+# App Title and Introduction
+st.title('Machine Learning Learning Hub')
+st.write('Welcome to the ML Learning Hub, your gateway to learning Machine Learning!')
 
+# Navigation and Layout
+section = st.sidebar.selectbox('Choose a Section', 
+                               ('Home', 'Beginner Resources', 'Intermediate Resources', 
+                                'Advanced Resources', 'Projects', 'Communities'))
+
+# Define Sections
+
+# Home Section
+if section == 'Home':
+    st.header('Welcome to the ML Learning Hub!')
+    st.write('Select a section from the sidebar to begin exploring resources.')
+
+# Beginner Resources
+elif section == 'Beginner Resources':
+    st.header('Beginner Resources')
+    st.write('Here are some great resources for ML beginners:')
+    st.markdown('[Machine Learning by Andrew Ng on Coursera](https://www.coursera.org/learn/machine-learning)')
+    st.markdown('[Introduction to Machine Learning for Coders by fast.ai](https://course.fast.ai/ml)')
+    st.markdown('[Google\'s Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course)')
+
+# Intermediate Resources
+elif section == 'Intermediate Resources':
+    st.header('Intermediate Resources')
+    st.write('Resources for those who are familiar with the basics:')
+    st.markdown('[Deep Learning Specialization by Andrew Ng on Coursera](https://www.coursera.org/specializations/deep-learning)')
+    st.markdown('[Kaggle Micro-Courses](https://www.kaggle.com/learn/overview)')
+    st.markdown('[DataCamp Machine Learning Scientist with Python Track](https://www.datacamp.com/tracks/machine-learning-scientist-with-python)')
+
+# Advanced Resources
+elif section == 'Advanced Resources':
+    st.header('Advanced Resources')
+    st.write('For those looking to deepen their ML knowledge:')
+    st.markdown('[Advanced Machine Learning Specialization on Coursera](https://www.coursera.org/specializations/aml)')
+    st.markdown('[MIT\'s Deep Learning for Self-Driving Cars](http://selfdrivingcars.mit.edu/)')
+    st.markdown('[The Elements of Statistical Learning: Data Mining, Inference, and Prediction](https://web.stanford.edu/~hastie/ElemStatLearn/)')
+
+# Projects
+elif section == 'Projects':
+    st.header('Projects')
+    st.write('Hands-on projects to apply your ML skills:')
+    st.markdown('[Kaggle Competitions](https://www.kaggle.com/competitions)')
+    st.markdown('[TensorFlow Projects](https://www.tensorflow.org/resources/learn-ml)')
+    st.markdown('[GitHub ML Showcase](https://github.com/collections/machine-learning)')
+
+# Communities
+elif section == 'Communities':
+    st.header('Communities')
+    st.write('Join ML communities to learn and share:')
+    st.markdown('[r/MachineLearning on Reddit](https://www.reddit.com/r/MachineLearning/)')
+    st.markdown('[Data Science Stack Exchange](https://datascience.stackexchange.com/)')
+    st.markdown('[AI & Machine Learning on Stack Overflow](https://stackoverflow.com/tags/machine-learning)')
+
+# Run the App
+# To run the app, save this script and use the command: streamlit run [script_name].py