Units:

1. Python for Data Science
   • NumPy, Pandas, and data manipulation fundamentals
2. Statistics & Math Foundations for ML
   • Probability, linear algebra basics, and statistical reasoning for models
3. Supervised Learning
   • Regression, classification, decision trees, and model evaluation
4. Unsupervised Learning
   • Clustering, dimensionality reduction, and pattern discovery
5. Introduction to Neural Networks & Deep Learning
   • Building and training a basic neural network
6. Data Pipelines & Model Evaluation
   • Feature engineering, overfitting, cross-validation, and metrics
7. Real-World Datasets & Tools
   • Working with scikit-learn, TensorFlow/Keras basics, and Jupyter notebooks
8. Capstone Project: End-to-End AI Application
   • From problem definition to trained model to presented results
9. AI Ethics, Bias & Responsible Deployment
   • Fairness, transparency, and real-world implications of AI systems
10. Preparing for Competitions & College Programs
    • Guidance for applying skills toward research, hackathons, and portfolios