Spaces:
Sleeping
Sleeping
import streamlit as st | |
# 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', 'Books', '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)') | |
#Books | |
elif section == 'Books': | |
st.header('Books') | |
st.write('Useful Texts:') | |
st.markdown('[Deep Learning](https://udlbook.github.io/udlbook/)') | |
st.markdown('https://www.deeplearningbook.org/') | |
st.markdown('https://tensornetwork.org/') | |
# 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 | |