Welcome to the Machine Learning in Action Lab! This tutorial is designed to help you apply theoretical concepts through hands-on projects. Let's dive into the essentials:
🚀 Getting Started
- Install Python and necessary libraries:
pip install scikit-learn numpy pandas matplotlib - Set up your environment using Jupyter Notebook or VS Code.
- Explore the Python Code Examples section for reusable scripts.
📊 Sample Project: Linear Regression
- Goal: Predict house prices based on features.
- Steps:
- Load the dataset with
pandas - Split data into training and testing sets
- Train a model using
sklearn.linear_model - Visualize results with
matplotlib
- Load the dataset with
- Linear Regression Visualization
🧪 Interactive Exercises
- Try modifying hyperparameters to improve model accuracy.
- Experiment with different datasets available in the Data Science Resources folder.
- Model Training Process
📚 Next Steps
- Deep Learning Fundamentals for advanced learners
- AI Ethics Guide to understand responsible AI development
Keep practicing and stay curious! 🚀